As noted earlier, our objectives are: (1) to assess the extent to which health differences explain the longevity gap; and (2) to quantify the fiscal consequences of gradually closing the health gap. To meet the first objective, we examine the 2004 cohort of 50 year-olds, and consider the counterfactual in which Americans have the same health as Europeans. We compare the resulting longevity estimate to longevity predicted using the baseline health status of Americans. We also assess the total differences in public spending generated by these underlying differences in health. The second objective requires that we analyze the consequences of gradually moving American health levels to those enjoyed by Europeans.
Explaining Differences in Longevity
We use the prevalence rates presented in to construct the first counterfactual cohort. We simulate the baseline outcomes of the cohort using the adjusted prevalence rates from European data. Using the methodology outlined, we preserve the correlation between health and other outcomes in the model. We keep other socio-economic characteristics constant at American levels, while varying the health status of the cohort. We then simulate transitions until everyone dies in the simulation. We compare this counterfactual with the status quo case where American health is unchanged.
Our baseline projection of remaining life-expectancy at age 50 is 31 years. This is very close to the 30.98 years estimated from life tables, as shown in . Assigning European health status levels to Americans increases healthy life expectancy (years without ADLs) by 1.3 years, and decreases unhealthy life expectancy (years with 1+ ADLs) by a tenth of a year (virtually zero). The overall effect is to increase life expectancy by 1.2 years, which is 92% of the difference in life expectancy reported in World Health Organization data. In other words, differences in health status explain nearly all the longevity difference across the US and Europe. Moreover, these findings indicate that worse health in the U.S. is associated with a loss of healthy life expectancy, rather than an increase in unhealthy life expectancy.
The Fiscal Consequences of Differences in Health
The differences in health across the US and Europe have important fiscal consequences. computes the overall fiscal effects on a per capita basis. Revenue would increase by $2,425 per capita, partly due to the increase in life expectancy and increase in earnings/labor force participation. On the expenditure side, there are two effects. First, old age pension benefits would increase by a substantial amount ($6,593 per capita). This is roughly the size of the average annual Social Security benefit payment. But, there would be a larger decrease in Medicare, Medicaid, and Disability insurance (DI) benefit payments: Total lifetime health-care expenditures would decrease by a stunning $17,791. This represents an 8.5% reduction in lifetime medical expenditures. The average reduction in lifetime payments is $4,717 for Medicare and $3,687 for Medicaid. Adding the reduction in DI costs, the net effect on government expenditures would be $2,477 per capita. Overall, the net fiscal impact of this scenario is an increase in per capita net revenue of $4,902 per capita for the government.
Per Capita Lifetime Fiscal Effects of European Health Scenario for Cohort in 2004
Long-Term Fiscal Consequences of US Health Improvements
The experiment we describe above computes the contribution of health differences between the US and Europe to differences in longevity and public spending. But we are also interested in the more practical question concerning the consequences of gradually moving cohorts of near-elderly Americans towards the health status of their European counterparts.
To implement a gradual scenario, we allow prevalence rates in the entering cohort to reach European levels by 2030. This is compared to a status quo scenario in which trends currently observed and projected among the new elderly persist. This scenario can be interpreted as a transition from the current steady-state where nothing is done, to a new one. provides the time path for various conditions among entering cohorts in the baseline and counterfactual scenarios. Each year, the population alive is representative of the age 50+ population alive in the U.S.
We report the results for the status quo in . Given current trends, we project the size of the population aged 50+ will increase by nearly 75% from 80.7 million in 2004 to 145 million in 2050. As a test of validity, we found that the forecast of 81.4 million 65+ year-olds in 2050 is very close to that of the Social Security Administration, which predicts 80.8 million. Life expectancy for people at age 50 is projected to increase from 31 years in 2004, to 31.6 years in 2050, a very modest increase.
Population Level Outcomes Under Status-Quo Scenario (2004–2050)
We compare our baseline projections to the scenario described in —gradual movement towards European prevalence levels. reports the results. Gradual health improvements would result in an aged 50+ population that is 4%, or 5.75 million, larger in 2050, than it would have been in the absence of those improvements. The population is also much healthier in 2030 and 2050 than under the status quo. For example, the obesity rate falls by 24 percentage points, while the prevalence of lifetime smoking and of diabetes both fall by roughly 10 percentage points each.
Figure 6 Source: Authors‘ calculations using the microsimulation model. Amounts in billion $2004 and represent the difference between the European scenario and the status quo scenario as defined in . Present discounted value calculated using a (more ...)
Population Level Outcomes under European Scenario (2004–2050)
Government revenue rises by 10%, or $30 billion, by 2050, as a result of the longevity gains. As before, there are two offsetting expenditure effects. On one hand, longer lives imply larger annuity burdens: OASI benefits go up by $70.4 billion. On the other hand, medical costs decrease by $124 billion, or 6.7%, in 2050. Medicare saves $36.4 billion, despite the increase in longevity. The overall effect on expenditures is initially negative, but turns positive by 2050. As found in Michaud et al. (2009), a transition to better health first decreases expenditures, but gains in longevity eventually exert upward pressure on spending.
shows that the gains in health expenditure materialize quickly, while the annuity burden takes longer to emerge. This is essentially a timing issue: cost savings due to lower disability appear before cost increases due to extensions in life expectancy. The total effect on expenditures is largest around 2030 and goes to zero by 2050. Since revenue rises as well, the net fiscal effect is positive in 2050 but slowly converges to zero. Hence, the transition to better health involves important fiscal effects, but these largely vanish once the new equilibrium is reached.
Figure 5 Source: Authors‘ own calculations using the microsimulation model. Health expenditures include Medicare and Medicaid. Social Security includes SSI, DI and OASI expenditures. Net Fiscal Impact is the revenue change minus the total expenditure change. (more ...)
shows that, in present value terms from the 2004 perspective, the increase in tax revenue almost entirely offsets the annuity burden. The effect on health expenditures remains. The present discounted value of Medicare and Medicaid savings combined is $632 billion, or 1.6 years of combined 2004 spending on the two programs. In terms of total medical spending, the present value of those savings is $1.1 trillion dollars. The fact that these are such large amounts illustrates the potential for savings by improving population health.
Robustness to Cross-Country Differences in Diagnosis
An alternative interpretation of cross-country differences in health focuses on differences in rates of diagnosis, rather than real differences in health status. The literature documents under-diagnosis of diseases like diabetes and hypertension in the US (Smith, 2007b
), but it is difficult to find comparable European studies. However, one direct analysis of this question suggests that differences in diagnosis are relatively modest. Banks et al. (2006)
compare objective clinical diagnosis among men, using commonly used thresholds on biomarkers, to self-reported measures of whether respondents have previously been diagnosed. For diabetes among those aged 40–70, they find a clinical prevalence of 4.8% in the UK and 8.9% in the US, but self-reported prevalence of 4.4% and 8.6%, respectively. The cross-country difference is similar using self-reports or clinical measurements (4.2% versus 4.1%). They find a similar result for hypertension. These discrepancies are not large relative to the differences we observe in the data.
Of course, the Banks et al. evidence is somewhat narrow in its focus on diabetes and hypertension, and on the US-UK difference specifically. Differences in screening and diagnosis might be more important for other diseases like cancer, as documented in Howard et al. (2009)
. To test the sensitivity of our results to diagnostic differences, we ran the cohort analysis under various assumptions about differential diagnosis. We allowed the “diagnosis effect” to account for between 0% and 100% of the total difference in measured health across the US and Europe, in 6 alternative sensitivity analyses. In these sensitivity analyses, we kept differences in obesity and smoking constant, as differential reporting across countries seems less clearly linked to systematic institutional factors. We calculate that if all of the difference we observed was due to under-reporting of disease in Europe, US life expectancy would increase by 0.25 years, as a result of differences in baseline obesity and smoking. On the other hand, if there are no differences in the rates at which diseases are diagnosed across countries, the effect is 1.2 years. If the Banks et al. result holds more generally, and there is at most a 5% difference in diagnosis, the effect drops to about 1.1 year. This suggests that the differences in health are likely to remain meaningful under reasonable assumptions about differential diagnosis.