The notion of the socio-economic ‘gradient’ in mortality is often discussed as if it were linear or near-linear, with much being made out of the fact that the increased risk associated with lower income holds, regardless of where one is on the income distribution. Our analyses do not support this claim. The shape of the association between income and mortality is strongly non-linear, with a much stronger association at low levels of income. Our results also indicate that the relationship between income and mortality in the USA is not invariant across time. While consistently concentrated in the bottom half of the income distribution, the increased risk of death associated with having less income applied to an increasing proportion of the US population over time, and the risk for the poorest in the country relative to those in the middle of the income distribution increased sharply over this time period.
Our results are consistent with previous findings of a non-linear association between income and mortality in the USA.6–8
Our analyses expand upon these findings by estimating the location of non-linearities in this relationship and examining changes in the association across three decades. We also utilize continuous detailed income data from the high-quality PSID, which is not limited by income top-coding as in previous studies.
There are two striking findings of these analyses and both can be used to reflect on the potential mechanisms that link income and risk of death, and policies that can reduce income-associated deaths. First, the results underscore the non-linearity of the association between income and mortality. Additional income makes very little difference in risk of death beyond a certain level of income, but makes a great deal of difference below that level. As a result, deaths are not distributed evenly across the income spectrum and cluster below the knots that we have discovered. In 1970–79, 25.0% of the deaths occurred below the knot at the ninth percentile. In 1980–89, the knot increased to the 20th percentile and 38.8% of deaths fell below the knot, and in 1990–99, 52.5% of deaths fell below the knot at the 32nd percentile in the income distribution.
Secondly, concurrent with this increase in the breadth of the effect of income on mortality and the proportion of deaths that occurred in the range of income most sensitive to income effects, there was also an increase in the relative risk of death for those who were poorest. In 1970, those at the fifth percentile of the income distribution had 2.6 times the risk of death over the next 10 years compared with those with incomes at the median, and in 1990 this increased risk was over seven times that of those at the median income level. Our analysis sample size of 12
167 individuals and 1057 deaths contributed to wide confidence intervals (CIs) in estimating the location of non-linearities in the income–mortality association, but we could still say with statistical confidence that the location of the non-linearity moved significantly outwards between 1970 and 1990.
Our results indicate that the effect of income on risk of death became both broader and deeper during these three decades. During this period there was also an increase in income inequality, with Gini, mean logarithmic deviation and Atkinson measures of inequality of household income also rising during this period.16
Our data are not adequate to explore the reasons why increased income inequality might be associated with widening and deepening of the association between income and mortality. However, the results are consistent with a picture of a middle class that is progressively losing ground economically, the poor and near-poor finding it increasingly hard to meet their needs, and health suffering as a consequence.
These analyses are relevant but are not determinative with regard to arguments about the causal impact of income on health. They establish more firmly the nature of the association between income and mortality and changes in the shape of this association over time. Any convincing argument for a causal or non-causal interpretation of the income–mortality association must be able to match the observed non-linearity and changes in knot locations over time. These results also call for future analyses of the patterns of risks and resources available to households at various locations in the income distribution, and how those have changed over time. Our analyses do not specifically address, for instance, the possibility that poor health adversely impacts earnings, so-called ‘reverse causality’. We chose not to exclude deaths that occurred in the first years of follow-up in order to provide the clearest description of the shape of the income–mortality association, regardless of the direction of causality. Whatever the direction of effect, competing explanations will need to explain the shape of the association and its changes over time. As mentioned above, if reverse causality were to be an important mechanism in the overall association, it would need to have become increasingly salient over time in order to explain the change in the location of non-linearities over time, which seems unlikely. Our results were also robust to using 5-year averages of income instead of 1 baseline year as an income measure.
The current results are also relevant to discussion of how we can reduce socio-economic inequalities in health. Focus on the gradient as if it is near-linear obscures the concentration of considerable risk for certain groups. While there may be arguments for policy approaches to reducing health inequalities that appeal to the broadest spectrum of the population, it is not accurate to base such an argument on increased income-related risk of death. On the other hand, the current results indicate that an increasing proportion of the population is feeling the impact of income-associated risk (32% in 1990–99), whereas at the same time the very poor are falling increasingly behind health-wise. Based on our analyses, a focus on the bottom 30% of the population, with additional emphasis on the poor and working poor, would seem to return the greatest benefits in reducing socio-economic inequalities in health, and there are numerous policy instruments that can be brought to bear to potentially improve their health.17