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1. doi: 10.1377/hlthaff.27.2.350 Health Aff March 2008 vol. 27 no. 2 350-360

# The Gap Gets Bigger: Changes In Mortality And Life Expectancy, By Education, 1981–2000

1. Ellen R. Meara,
2. Seth Richards and
3. David M. Cutler

+ Author Affiliations

1. Ellen Meara (meara@hcp.med.harvard.edu)

## Abstract

In this paper we examine educational disparities in mortality and life expectancy among non-Hispanic blacks and whites in the 1980s and 1990s. Despite increased attention and substantial dollars directed to groups with low socioeconomic status, within race and gender groups, the educational gap in life expectancy is rising, mainly because of rising differentials among the elderly. With the exception of black males, all recent gains in life expectancy at age twenty-five have occurred among better-educated groups, raising educational differentials in life expectancy by 30 percent. Differential trends in smoking-related diseases explain at least 20 percent of this trend.

During a period of increased focus on disparities in health, virtually all gains in life expectancy occurred among highly educated groups.

Disparities in mortality and morbidity across U.S. educational groups have been prevalent for decades. Attention to these disparities intensified with Evelyn Kitagawa and Philip Hauser’s comprehensive national account of educational mortality differentials in 1973, and a wave of related disparities research followed.1 Heartened by early successes in the Healthy People 2000 initiative to reduce disparities in health care and health outcomes, the Healthy People 2010 initiative aims to eliminate health disparities entirely by the end of this decade. Groups such as the Institute of Medicine (IOM) and many private organizations have also promoted this agenda.2 In 2003, the Agency for Healthcare Research and Quality (AHRQ) produced its first annual National Healthcare Disparities Report to track racial and socioeconomic disparities in health and health care.3

### Health interventions directed at disparities.

Several health interventions have been designed to address racial and socioeconomic disparities in health. Well-known examples include the Medicare disproportionate-share hospital (DSH) program ($12.9 billion annually) and the State Children’s Health Insurance Program (SCHIP) and related Medicaid expansions ($7 billion annually).4 In addition, much of the $6.6 billion annual budget for the Health Resources and Services Administration (HRSA) targets disadvantaged populations via funding for community health centers and the Ryan White Comprehensive AIDS Resources Emergency (CARE) program for HIV/AIDS. The current wave of state initiatives to expand health insurance coverage explicitly targets children and adults with low socioeconomic status (SES). Finally, efforts to improve public health promotion and disease prevention activities have expanded over time, with a particular focus on risk factors that are prevalent among populations with low SES: tobacco use, obesity, and underuse of preventive and screening services.5

### Recent research.

Recent research on racial disparities demonstrates mortality gains for blacks relative to whites during the 1990s, but data on education-related disparities in mortality are not comprehensive.6 Most research measuring such disparities ends in the 1980s. These studies show widening differences in mortality by education attainment.7 Many of the programs noted above postdate that era, however. More recent evidence focuses on area-level disparities.8 But major migration and demographic changes mean that area-level analyses may understate or overstate the full extent of health disparities at the individual level. Another large study after 1990 pools data from 1959 through 1996, making it difficult to attribute trends to a single decade.9 Furthermore, analyses of death by cause can augment our understanding of education-related disparities in mortality because some causes of death are more amenable to intervention than others, suggesting different policy approaches to ameliorate disparities.10

In this study we analyzed life expectancy and mortality trends by education group and then estimated which diseases account for differential trends.

## Study Data And Methods

### Data sources.

We used two different sources of data to estimate mortality trends. We matched census population estimates to death certificate data in the Multiple Cause of Death (MCD) files for 1990 and 2000.11 The nearly universal sample of deaths makes the MCD attractive, but there are questions about the accuracy of education reporting on death certificates.12 To validate these data and to assess an earlier time period, we used the National Longitudinal Mortality Study (NLMS), which followed members of the March Current Population Survey (CPS) through subsequent deaths. The CPS data span 1981–85 and 1991–95, and deaths span 1981–88 and 1991–98.13 The initial CPS sample is noninstitutionalized. For this reason, mortality rates in the NLMS are below those in the MCD, which includes all deaths. We restricted analyses to non-Hispanic blacks and whites, to limit the impact of immigration on our estimates.14

### Measures of education.

NLMS and U.S. census respondents report education, while informants, usually next of kin, report education on death certificates. Previous studies document some inconsistencies between these methods—in particular, overstatement of high school graduation relative to incomplete high school experience on death certificates.15 As a result, we chose two broad categories of education where agreement is high: low education, which refers to twelve or fewer years of education; and high education, which indicates at least thirteen years of schooling. This cutoff yielded the following shares of high-education people in our study: 47.3 percent in 1990 and 54.5 percent in 2000 using census data, and 43.9 percent in 1981–88 and 56.1 percent in 1991–98 in the NLMS.

Because rising educational attainment over time could affect our results, we recomputed estimates after equalizing the shares in the high- and low-education groups. We did this by randomly reassigning some people with twelve years of education to the high-education group in the early period to match education shares in the latter period. The patterns we documented are robust to the changing composition within education groups.16

### Mortality rates and life expectancy.

For a given age-race-sex-education group (for example, white low-education women ages 65–74 in 1990), we divided the number of deaths by the group’s population to compute age-specific mortality rates. These were calculated for ages 25–84, since most education is completed by age twenty-five and few NLMS participants survived beyond age eighty-four. To incorporate mortality after that age, we assigned published death rates within age-sex-race groups.17 Period life expectancy was then constructed in the usual way.18

For 1990 and 2000 we estimated cause-specific mortality rates based on the five leading causes of death: diseases of the heart, cancers, cerebrovascular diseases, chronic obstructive pulmonary disease (COPD), and unintentional injuries.19 Because ample evidence implicates tobacco use as the underlying cause for a substantial portion of deaths, we distinguished lung cancer from other cancers, yielding six cause-of-death categories.20

### Analyses.

We computed life expectancy by education both within race-sex groups and for the entire study population, holding race and sex composition constant at year 2000 shares. We also calculated age-standardized mortality rates using ten-year age strata with the year 2000 population standard. The changes in these rates from 1990 to 2000 were decomposed by cause of death and age group.21 We computed standard errors for 1990, 2000, and changes over time using the Delta method, and NLMS variance estimates accounted for the complex survey design.22 Lastly, given the importance of causes of death for which smoking is a risk factor, we computed the sex-specific, age-adjusted share of the adult population (age twenty-five and older) reporting that they were “current smokers” for selected years between 1966 and 2002, in the annual National Health Interview Surveys (NHIS) from various years.

## Study Findings

### Life expectancy.

Between the 1980s and 2000, life expectancy increases occurred nearly exclusively among high-education groups (Exhibit 1⇓). Comparing 1981–88 with 1991–98, life expectancy at age twenty-five grew 1.4 years for high-education people but only 0.5 years for low-education people—a difference of 0.9 year (p = 0.014). Between 1990 and 2000, life expectancy grew 1.6 years for the high-education group but remained unchanged for the low-education group (p <0.001 for the difference). As expected, life expectancy in the MCD data was lower than in the NLMS data because of the inclusion of the institutionalized population. In both data sets, education-related gaps in life expectancy increased by about 30 percent. This similarity is striking, given the differences in sampling frames and education measures between the sources. Further, the levels of the education differences are large. In 2000, life expectancy for a twenty-five-year-old with a high school diploma or less was fifty years. For a person with some college, life expectancy was nearly fifty-seven years.

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EXHIBIT 1

Life Expectancy Among Americans At Age Twenty-Five, By Education Level, Selected Years 1981–2000

### Life expectancy by race and sex.

Widening educational disparities in life expectancy do not arise from increased race or sex differences in mortality trends. Our population estimates hold demographics constant at their 2000 level. More fundamentally, Exhibit 2⇓ shows similar trends within each race and gender group. The growing educational gap in life expectancy was most pronounced among women, regardless of race. Life expectancy at age twenty-five declined among less-educated black and white women while rising one year or more for more-educated women. By 2000, highly educated women could expect to live more than five years longer than their less-educated counterparts, in both races. For white men the gap was 7.8 years in 2000, up 1.6 years from 1990, and for black men it was 8.4 years, up 1.3 years.

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EXHIBIT 2

Life Expectancy At Age Twenty-Five, By Race, Sex, And Education Group, Selected Years 1981–2000

Overall and even within-race trends in mortality by education mask another important trend. Although SES differences in mortality were rising, mortality differences across sexes and races were falling. From 1990 to 2000, the life expectancy differences between blacks and whites decreased 1.8 years for low-education males, 1.5 years for high-education males, and 0.7 and 0.6 years for low- and high-education females, respectively. Life expectancy for low-education white males increased in both the NLMS and MCD data, while it decreased for females. Within the high-education group, life expectancy increased for both men and women, but the gains for men were almost double those for women. Gaps in mortality by race and sex persist, but they are smaller now than they were in the 1980s.

### Condition-specific contributors to rising education gaps.

Diseases of the heart, cancers, and COPD contributed more than 60 percent of deaths in the sample in 1990 and 2000 in all race and gender groups. Heart diseases and cancers excluding lung cancer contributed most to rising education differentials, 32 percent. Lung cancer and COPD, two diseases largely attributable to tobacco use, accounted for 21 percent (Exhibit 3⇓). These diseases were particularly salient among less-educated white women over age forty-five, among whom lung cancer and COPD contributed about 25 percent of rising educational gaps in mortality.23 By 2000, lung cancer and COPD death rates were twice as high among low-education white men and women and black men, compared to the more educated in these groups (Exhibit 4⇓).

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EXHIBIT 4

Age-Standardized Deaths Per 100,000 Americans, By Education, Race, Sex, And Cause, 1990 And 2000

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EXHIBIT 3

Contribution To Growth In Education-Related Disparities In Mortality Among Americans Ages 25–84, By Cause Of Death, 1990–2000

### Age-specific contributors to rising education differentials.

Increased education differentials among the elderly account for much of the growing gaps in mortality and life expectancy. Within each race-gender group, at least half of the growth in life expectancy gaps and more than 60 percent of the growth in mortality gaps come from those age sixty-five or older.24 In contrast, trends among adults ages 25–44 contributed little to growing gaps in mortality, and among black men ages 25–44, education-related mortality gaps narrowed during 1990–2000. Among the causes of death we examined, the narrowing educational differential among black men ages 25–44 was driven by unintentional injuries and heart disease deaths.25

### Smoking rates over time.

Adult smoking rates have declined greatly since the mid-1960s (Exhibit 5⇓). Among men, the reduction in smoking prevalence was relatively even by education, although somewhat greater for the better-educated. Between 1966 and 1995, rates dropped twenty-two percentage points among the better educated versus eighteen percentage points among less-educated men (Exhibit 5⇓), despite the fact that better-educated men already had lower smoking rates in 1966.

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EXHIBIT 5

Rates Of Smoking Among Americans Age Twenty-Five And Older, By Sex And Education Level, Selected Years 1966–2003

Smoking among women increased from 1966 through the late 1970s and then began to decline (Exhibit 5⇑). The decline from 1979 through 1995 was twelve percentage points among the better-educated and five percentage points among the less-educated. The sharp divergence in smoking rates by education occurred between 1979 and 1983, coincident with the first surgeon general’s report highlighting smoking risks for women.26 Over the entire period, smoking among female college attendees declined by more than sixteen percentage points, compared with only 7.5 percentage points among less-educated women (data not shown).

## Discussion

The 1980s and 1990s were periods of rapidly rising life expectancy, but the mortality declines that yielded these gains did not occur evenly by education group. On average, we find very little change in life expectancy among less-educated black and white non-Hispanics and very substantial increases in life expectancy among the more educated. These patterns mirror similar widening of education differentials in disability and self-reported health status over the same period.27 The growing gap in life expectancy by education occurred during a period of increasing attention to health disparities and increased public spending designed to improve the health of less-advantaged populations.

One important exception to this pattern is that education-related mortality disparities narrowed among young black men, a finding consistent with recent evidence that race-related mortality gaps narrowed in the 1990s. Nevertheless, a five-year gap in life expectancy between blacks and whites remains.28 In terms of gender, men made faster gains in mortality than women over this period, narrowing gender-related mortality and life expectancy gaps.

### The income factor.

Our data span a period of rapidly rising income inequality, providing one potential explanation for widening education disparities. However, data do not support this explanation because health disparities narrowed across race and sex as inequality increased.29

### The smoking factor.

Our results suggest that differential trends in smoking may explain a large part of widening gaps in mortality and life expectancy. The diseases contributing most to the growing education gap in mortality include diseases of the heart, lung and other cancers, and COPD, all of which share tobacco use as a major risk factor. Lung cancer and COPD alone account for one-quarter of the increasing gap in life expectancy for women over age forty-five, consistent with their sharp divergence in smoking rates during the 1980s. For men, the divergence in smoking rates was more moderate, as was the increase in the mortality gap attributable to tobacco-related causes of death.

Public policy designed to reduce health consequences related to smoking might have indirectly contributed to this disparity. In the half-century since the harms of smoking became widely known, tobacco control measures have proliferated. Cigarette labels warning of the health hazards of smoking have been required since 1966. Cigarette advertising was banned from television and radio in 1971.30 During the 1980s and 1990s, many states and localities instituted smoking bans in the workplace. By 1993, 70 percent of indoor workers had smoking bans in work, and by 2007, every state had some smoke-free air provision.31 In addition, cigarette taxes have increased rapidly in recent years, after falling in real terms in the 1970s. On net, the real price of cigarettes has nearly tripled since the 1960s.32

The proliferation of tobacco control policies brought remarkable reductions in tobacco use. In the four decades following the 1964 surgeon general’s report, per capita annual consumption of cigarettes among adults fell by half. However, declines were greatest among the most-educated groups. The growing education-related gap in mortality for smoking-related causes supports the long-standing paradox that prevention can widen disparities in health across education and income groups.33 In this case, the advances are related to knowledge about risk-factor control; we cannot say whether the same is true about medical technologies.34

### Other efforts to reduce disparities.

The focus on tobacco does not imply that other efforts to reduce disparities in health were not successful. Indeed, we confirm recent work highlighting relative gains in life expectancy overall for blacks compared with whites during the 1990s.35 Further, other studies have shown that Medicaid expansions targeting low-income children and pregnant women improved their health outcomes.36 Our study does not argue that these policies were unsuccessful. Rather, it suggests that these efforts were swimming against a strong tide, one that overwhelmed billions of dollars spent annually on additional medical care. On a more positive note, our results suggest that one place to look for real progress is tobacco control efforts for low-SES groups because mortality trends mimic trends in smoking that occurred decades earlier. These long-run consequences of health behavior bolster the argument for early childhood intervention.37

### Why differential smoking trends?

The explanation for differential smoking trends is complex. Basic knowledge does not appear to be the major issue. In 1986, 90 percent of Americans surveyed across the board reported that smoking causes lung cancer and emphysema, and 80 percent believed that it contributed to heart disease and bronchitis.38 Translating knowledge into action has proved more complex, however. Innovations that target less-advantaged groups might offset this unintended consequence of medical progress. Some caution about this conclusion is needed, however. Without addressing the underlying factors that lead less-educated people to be less able or willing to invest in better health, measures to reduce smoking may simply lead to a shift from tobacco-related deaths to other causes.39

### The obesity factor.

Beyond the differential change in smoking, there is the national trend toward increased obesity. As with smoking, obesity is more common among the less-educated than among the better-educated. Further, recent research suggests that obesity might contribute to nearly as many deaths as tobacco does.40 Although the population health consequences of obesity remain controversial, the obesity trends into the future could further widen socioeconomic gaps in health.

In summary, during a period of increased focus on disparities in health, virtually all gains in life expectancy occurred among highly educated groups. Causes of death related to differential trends in cigarette smoking by education have contributed greatly to rising mortality differentials. Larger and better-targeted efforts to push successful health interventions into less-educated groups may be needed to achieve the goal of reducing socioeconomic disparities in health.

## Footnotes

- Ellen Meara (meara@hcp.med.harvard.edu) is an assistant professor of health economics in the Department of Health Care Policy, Harvard Medical School, in Boston, Massachusetts. Seth Richards is a doctoral candidate in economics at the University of Pennsylvania, in Philadelphia. David Cutler is a professor of economics at Harvard University, in Cambridge, Massachusetts.

- The authors gratefully acknowledge funding from the Russell Sage Social Inequality and Health Project, National Institute on Aging Grant nos. P30-AG012810 and P01-AG005842, and National Institute on Drug Abuse Grant no. 5K01DA19485-2. Nicholas Christakis, Christopher Jencks, and Kirsten Smith provided helpful comments on the manuscript, and the authors thank Norman Johnson, Eric Backlund, and U.S. Census Bureau staff for producing estimates from the National Longitudinal Mortality Study.

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## Responses on This Article

- Closing The Gap Should Be Getting Easier
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  Health Aff published online March 11, 2008
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