U.S. Life Expectancy Turns Back Up

As Covid deaths fall, the expected average lifespan rises to 78.4 years

WSJ editorial. 

"Some good news as 2024 nears the end: Life expectancy in the U.S. last year made an unusually sharp increase as deaths from most major causes declined, according to the latest Centers for Disease Control and Prevention report. Americans can expect more longevity gains in the future—as long as Washington doesn’t introduce harmful policies.

Life expectancy in 2023 rose 0.9 years to 78.4 while the overall mortality rate adjusted for age declined 6%. Death rates among all age groups fell, and more sharply for middle-aged Americans and seniors. A typical 65-year-old can expect to live another 19.5 years, up from 18.9 years in 2022.

The large rebound in a single year owes largely to a decline in Covid deaths as the pandemic receded into the past. Covid deaths last year were roughly the same as those from the flu during a bad flu season. Death rates from cancer, heart disease, diabetes, Alzheimer’s and unintentional injuries (e.g., drug overdoses) also declined.

It’s true that U.S. life expectancy is still lower, and deaths from most causes somewhat higher, than before the pandemic when it reached an overall average of 78.8 years. But that’s because of an increase in chronic illnesses, which may have been exacerbated by the pandemic lockdowns. Forced to stay home, many Americans ate and drank more and used more drugs.

The Biden Administration claimed credit for the lifespan increase because drug overdoses declined slightly in 2023. Perhaps political attention to the fentanyl scourge is making a difference. But overdoses were still 50% higher last year than in 2019. The truth is that the Administration’s “harm reduction” policies—e.g., distributing sterile needles and opioid-overdose medicine naloxone to addicts—have failed to reduce addiction.

A common lament on the political left and right is that the U.S. has a lower life expectancy despite spending more on healthcare than most developed countries. But America also has more chronic disease and drug addiction, which aren’t from failings in private healthcare. Americans have access to more treatments than any country in the world.

This is why U.S. cancer survival rates are higher than in most developed countries and continue to improve. Personalized cancer vaccines and CAR T-cell therapies have shown potential to treat deadly cancers like pancreatic and glioblastoma. GLP-1 medicines like Ozempic could help extend lifespans by reducing obesity, diabetes and even drug addictions.

The policy risk is that government drug price controls will discourage innovation. Expanding government control over healthcare isn’t the way to make Americans healthier."

The Underappreciated Trend in Mortality and Inequality

By Vincent Geloso. He is a professor of economics at King’s University College.

"Most economists, left or right, care about human development. By human development, they mean more than simply increases in income. They refer to a greater ability for individuals to choose the lives they deem most fulfilling under continually weakening constraints. Regardless of their political leanings, most economists will also be concerned with the inequalities in human development. 
This sort of inequality is hard to measure. Generally, we concentrate on income inequality to measure those inequalities. This tends to create false impressions about how equal the world has grown since the early 19^th century. In fact, numerous indicators suggest that the world is now more equal than it was in the past!

Concentrating solely on incomes is bound to have shortcomings. The issue with income is that the levels capture both the opportunities available to workers and the decisions of workers. For example, it is well-known that after a certain wage level, workers will use wage increases to substitute leisure for paid work time.

This is known as the backward bending labor supply curve. However, the curve is not the same for everyone. Some workers simply decide to work more than others (or have incentives to do so). This is why we observe rising inequality in working hours in richer countries. In a situation like this, how can we assess the inequality in human development (i.e. inequality in our ability to make choices)?
Measures such as the human development index (HDI) use a broader set of indicators to capture human development. One such indicator is life expectancy at birth. It is taken as a proxy for how healthy our lives are. The intuition is that the healthier we are, the more we are able to make choices. If life expectancy at birth can be taken as a reliable indicator of health outcomes broadly defined, inequalities in life expectancy will be relevant to inequality in human development.

What do such measures say? Using demographic data accessible to all, Sam Peltzman made the exercise of measuring inequality in life expectancy in a 2009 article in the Journal of Economic Perspectives. He calculated the Gini coefficient for that indicator since the late 19^th century for many countries and as far back as 1750 for a few countries such as Sweden and Germany. The Gini coefficient takes a value of zero if there is perfect equality and a value of one if there is perfect inequality.

What does his exercise yield? The Gini coefficient for Sweden, England, France, Germany and the United States stood between 0.4 and 0.5 for most of the 19^th century. However, there was a clear downward trend in mortality inequality so that by 1900, the level had fallen to a range between 0.3 and 0.4. By 1950, the drop had continued and stood instead between 0.1 and 0.2. Today it is closer to 0.1. Similar declines are observed in countries like India, Brazil and Japan over the course of the 20^th century.

In fact, Peltzman points out that in some countries like India and Brazil, “mortality is distributed more than income.” This is a momentous collapse in the inequality in life expectancy. Peltzman made a similar exercise using life expectancy for American states starting in 1910 and found a marked decline in life expectancy inequality within the United States.

What used to be a major source of inequality is now a minor source of inequality in human development. The unhealthy focus on income inequality makes us blind to these great developments in human well-being. This is not to say that analysis of income (or wealth) inequality should be abandoned. However, it ought to be complemented with other indicators of inequality. A great number of indicators would constitute a dashboard for sober analysis. At the very least, it would give us the capacity to appreciate how we are living in a more equal and richer world than we used to before."

Is life expectancy really falling for groups of low socio-economic status? Lagged selection bias and artefactual trends in mortality

From the International Journal of Epidemiology. By Jennifer B Dowd and Amar Hamoudi. Excerpts:

"We suggest that it is long past time to admit an alternative—and arguably more plausible—interpretation of these patterns. The fact that a measure was computed at two different time points does not, by itself, make the difference between them a trend. Imagine if researchers measured the average temperature for the whole of the USA a decade ago, and then for only Alaska this year, and found the former number to be lower than the latter. Would it be appropriate to say that average temperatures had ‘declined’ over the decade? We argue that it would not, and that it is likewise not appropriate to be describing many of the observed differences in subgroup life expectancy or mortality as ‘trends’."

"Our concern is that stable differences between noncomparable subgroups are being mistaken for time trends in a broader group—a phenomenon we term lagged selection bias (LSB). We use the term ‘selection bias’ in a spirit that is common in the population sciences,⁸ although in epidemiology the phenomenon might also be understood by some as a form of collider bias and by others as a form of confounding.⁹ Regardless of how it is labelled, LSB generally occurs when: (i) there is a temporal lag between when individuals are selected into their exposure group and when the outcome manifests; and (ii) the dynamics of selection into the exposure group were changing over the period spanned by the lag. Due to this temporal lag, the implications of changing selection dynamics only become manifest long after the changes have happened. When these conditions apply, it is impossible to tell from the data alone whether contemporary differences in the outcome indicate contemporary trends (like increasing social exclusion of high school non-completers), or changes from a long time ago in the exposure selection process, or some complicated combination of these two. A proper interpretation, therefore, must be explicitly based on the history of the selection process itself."

"These ‘trend’ interpretations gloss over important differences between demographic constructs like period age-standardized mortality rates or life expectancy and real-world outcomes like the risk of death or expected length of life. They also give short shrift to two critical facts of the social history of the USA: 

1. Lag between exposures and outcome. High school completion in the USA has historically been determined by the time a person is in his or her early 20s,¹⁴ long before the ages when most mortality is observed.
2. Secular change in the dynamics of selection into the exposure group. Access to high school increased dramatically over the 20th century in the USA (Figure 1 ). The average White girl born at the end of World War I stood about a 50% chance of finishing high school by her 20th birthday; born at the end of the Lyndon Johnson administration, she had a 90% chance. Much of this expansion in access was driven by changes in the opportunity costs facing working class families who wanted to send their children to high school;^15–17 as access expanded, it likely became more equitable."

When one computes mortality rates or life expectancy for the year 1990 for White female high school non-completers, one ascribes to those reaching age 70 the risk of dying that was faced by a woman who was excluded from high school around the end of the Great Depression—a normative experience for her time. By contrast, the same exercise for the year 2010 ascribes mortality risk at age 70 based on the experience of a woman excluded from high school in the late 1950s/early 1960s—which means she was left behind during a period of unprecedented expansion in access to secondary education. 

The high school completion status of those dying in a given year is like light from a distant star—it reflects social conditions that prevailed decades before. In terms of mortality risk, those excluded from high school in the early part of the 20th century are not comparable with those excluded from high school a generation later, because those left behind by the high school expansions in mid century likely had childhoods that were more disadvantaged along many dimensions, and so were at higher mortality risk all along. Life expectancy among high school non-completers for the year 1990 will largely be determined by the mortality experience of the relatively lower-risk subgroup; for the year 2010, it will be determined by the mortality experience of the higher-risk subgroup. Describing differences between these two subgroups as a ‘decline’ in the life expectancy of high school non-completers simply because they were born at different times almost certainly reflects LSB."

"To illustrate the dynamics more clearly, we have used US population data and forecasts to recreate the mortality experience of 141 birth cohorts of women from 1880 to 2020. The overall risk of dying at each age for women in each cohort is based on actual and forecasted data from the US Social Security Administration.¹⁹ We also capture the social gradients around these average risks by randomly assigning each simulated person to one of 1001 early life socioeconomic status (EL-SES) categories and assigning those who are more (less) disadvantaged in terms of EL-SES to a higher (lower) than average risk of dying at any age. These disparities are based on patterns observed in the USA.^20,21 We also incorporate an EL-SES gradient in access to high school reflecting historical patterns.¹⁶ In cohorts for whom high school completion is rare, it is only the most advantaged young adults who achieve it; as access expands, it also becomes more equitable. Our example rules out an increase in actual mortality risk for anyone—every individual stands a lower chance of dying at every age if they are born later, than they would have stood if they had been born earlier but had exactly the same characteristics. For example, in our simulation the average person in the most disadvantaged quintile in 1990 dies at age 69.5 years and in 2010 at age 72.2 years"

"Life expectancies at age 25 for the bottom quintile of the EL-SES distribution as well as for high school non-completers are shown in Figure 3. When we identify disadvantage based on the stable EL-SES characteristic, period life expectancies at age 25 are 43.8 years in 1980, 44.2 years in 1990, 44.4 in 2000 and 44.7 in 2010. When we rely on people’s access to high school to identify their exposure, the corresponding values are 48 years, 47.3 years, 45.3 years, and 43.4 years. Age-standardized mortality rates, shown in Figure 4, move in parallel with period life expectancies; this reflects the fact that the two demographic constructs are computed in very similar ways."

"This difference arises because of a policy success—namely, improvements in equity of access to education over the 20^th century. As a result of that policy success, people who were already vulnerable to shorter lifespan because of conditions in their early lives nonetheless had access to high school, whereas those exposed to similar conditions from an earlier birth cohort had no such access. As a result, averages for high school non-completers in earlier years include the less vulnerable, as well as the very vulnerable; in the later years, the less vulnerable were simply reclassified to the high-school completer group, leaving behind only the very vulnerable. It is that reclassification that drives the artefactual ‘decline’ in life expectancy a half-century later, not any change in anyone’s actual risk of dying. 

More than simply illustrating the important distinction between real-world health outcomes like longevity and demographic constructs like period life expectancy, the exercise underlying Figures 2–4 helps pin down the timing when we would expect to start seeing artefactual ‘trends’ in the demographic constructs, given the history of educational expansion in the USA over the 20th century. The exercise suggests that, if health disparities have been large and stable over the past century, and given trends in overall mortality risk and educational expansion over the past century, one would expect to see an artefactual ‘rise’ in period age-standardized mortality rates among the least educated, starting in the 1990s and continuing for about a generation. This matches the patterns that have been reported."