Background Mortality from all causes is higher for persons withfewer years of education and for blacks, but it is unknown whichdiseases contribute most to these disparities.
Methods We estimated cause-specific risks of death from datafrom the National Health Interview Survey conducted from 1986through 1994 and from linked vital statistics. Using these riskestimates, we calculated potential years of life lost and potentialgains in life expectancy related to specific causes, with stratificationaccording to education level and race.
Results Persons without a high-school education lost 12.8 potentiallife-years per person in the population, as compared with 3.6for persons who graduated from high school (ratio, 3.5; P<0.001).Ischemic heart disease contributed most (11.7 percent) to thedifference according to education in potential life-years lost(with all cardiovascular diseases accounting for 35.3 percent).All cancers accounted for 26.5 percent, including 7.7 percentdue to lung cancer; other lung diseases and pneumonia contributed10.1 percent of the total, whereas human immunodeficiency virus(HIV) disease accounted for none of the difference accordingto education. The pattern of disparities according to levelof income was similar to that according to level of education.Blacks and whites lost 7.0 and 5.2 potential life-years perperson, respectively, as a result of deaths from any cause (ratio,1.35; P<0.001). Cardiovascular diseases accounted for onethird of this disparity, in large part because of hypertension(15.0 percent); HIV disease (11.2 percent) contributed almostas much as ischemic heart disease (5.5 percent), stroke (2.8percent), and cancer (3.4 percent) combined; trauma and diabetesmellitus accounted for 10.7 percent and 8.5 percent, respectively.
Conclusions Although many conditions contribute to socioeconomicand racial disparities in potential life-years lost, a few conditionsaccount for most of these disparities — smoking-relateddiseases in the case of mortality among persons with fewer yearsof education, and hypertension, HIV, diabetes mellitus, andtrauma in the case of mortality among black persons. These findingshave important implications for targeting efforts to reduceexisting disparities in mortality rates.
Mortality rates in the United States have declined dramaticallyover the past century. Yet persons with fewer years of educationand black persons still live approximately six fewer years thanbetter-educated persons and whites, respectively.1,2 Consequently,the Healthy People 2010 initiative3 has made the eliminationof disparities in health its primary goal.
The task of eliminating health disparities seems overwhelming,since minorities and the less educated have higher mortalityrates for a wide range of diseases, including stroke,4,5 diabetes,6,7cancer,8,9,10,11 heart disease,12,13,14,15 the acquired immunodeficiencysyndrome (AIDS),16,17 and lung disease.11,18 However, we mightachieve greater success by targeting the diseases that havethe greatest influence on disparities in mortality. In thisstudy, therefore, we estimated the differences associated withrace and level of education in terms of potential life-yearslost and potential gains in life expectancy related to specificcauses, with the goal of determining which contribute most tothe disparities.
Methods
Study Design
Using nationally representative data, we estimated cause-specificrisks of death among adults in the United States. These estimateswere then used in a simulation model to calculate the differencesaccording to level of education and race in potential yearsof life lost and potential gains in life expectancy. Both statisticsmeasure the influence of different causes on premature death.Potential life-years lost are the years a person would havelived (up to an age cutoff) had he or she not died, with thenumber of years attributed to a specific cause.19 The potentialgain in life expectancy is the increase in life expectancy thatwould result if a specific cause of death were eliminated. Unlikethe former method, it does not assume that the person wouldhave survived until the cutoff age.
Sources of Data
We examined data from the National Health Interview Survey conductedfrom 1986 through 1994; this survey is conducted annually ona cross-sectional probability sample of U.S. households representingthe civilian, noninstitutionalized population.20 The NationalHealth Interview Survey contains demographic and health informationon each household member. This information is linked to dataon mortality from the National Death Index (with causes codedwith the use of the ninth revision of the International Classificationof Diseases [ICD-9]) through December 1997.21
From the sample of 1,009,451 persons, we excluded 281,491 personsless than 18 years of age (27.9 percent of total) because dataon deaths in this age group were unavailable. We excluded 23,902persons (2.4 percent) with insufficient data for adequate matchingwith the National Death Index data,21 4848 persons (0.5 percent)with missing data on education, and 77,812 persons (7.7 percent)who were not classified as either black or white.
Data on mortality from the National Health Interview Surveyextend only to 1997 and thus do not reflect recent improvementsin mortality rates among patients with AIDS.22 From 1994 to1999, the annual mortality rate for patients with AIDS droppedby 75.7 percent for white men, 55.8 percent for black men, 59.5percent for white women, and 42.1 percent for black women.17According to these trends, we proportionately decreased estimatesfrom National Health Interview Survey data regarding the riskof death from AIDS.
Estimating Cause-Specific Mortality
We used the Kaplan–Meier product-limit method to estimatethe one-year risk of cause-specific death for each subgroupdefined by age, race, sex, and education.23 We dichotomizededucation using a cutoff of completion of high school, and weused one-year age strata. We examined the underlying cause ofdeath, determined by an algorithm that identified the single,initiating cause ultimately leading to death from among allcauses listed on the death certificate.1,24 We examined deathsfrom cardiovascular disease, cancer, infection, diabetes mellitus,renal disease, liver disease, rheumatologic disease, alcohol-relateddiseases, trauma (accidents, suicide, and homicide), and morespecific types of cardiovascular disease, cancer, and infection.The remainder of deaths were categorized as due to other causes.Using locally weighted scatter-plot smoothing,25 we smoothedthe hazard probabilities according to cause of death and stratadefined by sex, race, and education.
For the survival analyses, we used weights made up of threecomponents. We used original probability weights from the NationalHealth Interview Survey, which account for the sampling methods.We also used an "ineligibility weight," equal to the inverseof the predicted probability of linkage with the National DeathIndex (based on a logistic-regression analysis that used age,sex, race, and income as predictors). Thus, subjects who weredemographically more similar to ineligible persons were assignedgreater weights. Each yearly sample represents the total U.S.population. Thus, combining nine samples (those for 1986 through1994) would result in estimates for a sample nine times as large.We therefore used a third weighting method26 to adjust for thecombining of nine National Health Interview Survey samples.This weight equals the proportion of the estimated populationfor a particular yearly sample to the combined estimated populationfor 1986 through 1994. Thus, a National Health Interview Surveysubject in 1986 had a cohort weight of 0.12 (2.36x108 subjectsin the estimated population of the 1986 National Health InterviewSurvey divided by 1.97x109 subjects in the combined estimatedpopulation).
Estimating Potential Life-Years Lost and Potential Gains in Life Expectancy
Estimates of cause-specific risks of death were used in a state-transitionMonte Carlo simulation to model deaths among adults in the UnitedStates. Persons entered the simulation at the age of 25 yearsand were followed in one-year cycles until death. At each cycletransition, individual persons either survived to the next year(thus aging one year) or died from a specific cause. A set ofrisks of cause-specific death estimated as a function of age,sex, level of education, and race (as described above) determinedeach transition.
To compare deaths according to level of education, we simulateda corresponding population of persons with more and less educationwho were 25 years of age (100 million in each group) with thesex and race distribution of the total adult population of theUnited States, on the basis of data from the 2000 National HealthInterview Survey. This simulated population consisted of whitemen (42.0 percent), black men (5.7 percent), white women (45.2percent), and black women (7.0 percent). To compare deaths accordingto race, we used a simulated population of black persons andwhite persons 25 years of age that included men with more education(high-school graduation or greater, 38.3 percent), men withless education (no high-school graduation, 9.5 percent), womenwith more education (42.1 percent), and women with less education(10.2 percent).
From this simulation, we estimated potential life-years lostas the difference between age at death and the maximal numberof years a person could have lived, which we set at 75 (i.e.,no life-years were considered lost after the age of 75 years).We estimated the potential gain in life expectancy for eachcause of death as the change in life expectancy when the mortalityfor that particular cause was set to zero. To make the resultsof the calculations for potential gains in life expectancy andpotential life-years lost comparable, we truncated life expectancyat the age of 75.
We estimated standard errors using percentile nonparametricbootstrap methods with 1000 repetitions.27 Because of the largesample, we estimated 99 percent confidence intervals. We usedStata version 7.0 software for survival analyses and SAS version8.0 for smoothing hazard probabilities and for the simulation.
Sensitivity Analyses and Validation of the Simulation Model
Estimation of potential life-years lost before the age of 85,the use of other cutoffs for education categories, and categorizationof education into six groups did not significantly change theresults. We also compared income groups, using annual familyincome (<$20,000 vs. $20,000) and the poverty-line cutoff,which is based on family income adjusted for household size.Results for income were unaffected by the use of other cutoffsand were similar to the disparities according to level of education.
Errors in the identification of the underlying cause of deathmay occur. In particular, death from ischemic heart diseasemay be misattributed to hypertension, since these are commoncoexisting conditions. Our results were unchanged when we recategorizeddeaths from hypertension as deaths from ischemic heart diseasewhen ischemic heart disease was listed anywhere on the deathcertificate.
To validate the simulation model, we compared the results ofthe simulation with empirical estimates for the combined surveycohort (with an average of 7.4 years of follow-up). We repeatedthe simulation model for seven annual cycles, using a populationsimilar to the National Health Interview Survey cohort in age,sex, race, and level of education. The simulation and the empiricalresults were similar.
Results
Educational Disparities in Potential Life-Years Lost
When adjusted for age, sex, and race, the number of potentiallife-years lost from all causes of death was 3.5 times as greatfor persons with less education than for persons with more education.Persons with less education and those with more education lost12.8 and 3.6 potential life-years before 75 years of age perperson, respectively, a difference of 9.2 years (99 percentconfidence interval, 8.5 to 10.7) (Table 1). Less-educated personslost more potential life-years than more-educated persons forevery specific cause we examined, though not all differenceswere statistically significant. Ischemic heart disease contributedmost to the educational disparity in life-years lost (11.7 percentof the total difference in life-years lost), followed by lungcancer (7.7 percent), stroke (5.8 percent), congestive heartfailure (5.1 percent), pneumonia (5.1 percent), and lung disease(5.0 percent). Hypertension contributed only 3.5 percent, andno difference due to infection with the human immunodeficiencyvirus (HIV) was observed.
Table 1. Racial and Educational Disparity in Potential Life-Years Lost per 1000 Persons before the Age of 75.
Racial Disparities in Potential Life-Years Lost
When adjusted for age, sex, and level of education, the numberof potential life-years lost from all causes of death was 35percent greater for blacks than for whites. Black persons andwhite persons lost 7.0 and 5.2 potential life-years before theage of 75 per person, respectively, a difference of 1.8 years(99 percent confidence interval, 1.4 to 2.8).
Blacks fared worse than whites for the majority of specificcauses that we examined (Table 1). Death from hypertension contributedmost to the racial disparity in potential life-years lost (15.0percent), followed by HIV disease (11.2 percent), diabetes (8.5percent), and homicide (8.5 percent). These estimates have beenadjusted for recent declines in mortality from HIV.17
Of the major categories of disease, cardiovascular disease contributedmost to the disparity in mortality from any cause (34.0 percent),followed by infection (21.1 percent) and trauma (10.7 percent).Cancer contributed only 3.4 percent to the racial disparityin potential life-years lost, even though cancer was the predominantcause of death among white persons (33 percent of the totallife-years lost) and the second most common cause among blackpersons (25 percent). Deaths from cardiovascular disease rankedfirst among black persons (31 percent) and second among whitepersons (30 percent). All results were similar when potentiallife-years lost before the age of 85 years, rather than theage of 75 years, were examined.
Potential Gains in Life Expectancy
For each person, potential life-years lost was attributed toa single cause. Had the person not died from that cause, however,he or she might have died prematurely from another cause. Consequently,potential life-years lost may not accurately account for "competingrisks" of death and may give excessive weight to causes of deathoccurring at younger ages.28 The measure of potential gainsin life expectancy avoids this limitation by estimating thechange in life expectancy that would result if a particularcause of death were eliminated.29
The disparity in life expectancy according to educational level(up to 75 years of age) was 9.19 years overall (8.71 years amongblacks and 9.27 years among whites). The elimination of ischemicheart disease would result in the biggest change, decreasingthe disparity according to educational level to 8.35 years (adecrease of 0.84 year per person), followed by lung cancer (decrease,0.54 year), stroke (decrease, 0.42 year), pneumonia (decrease,0.37 year), congestive heart failure (decrease, 0.36 year),lung disease (decrease, 0.36 year), colon cancer (decrease,0.32 year), diabetes (decrease, 0.28 year), hypertension (decrease,0.25 year), breast cancer (decrease, 0.19 year), and leukemiaor lymphoma (decrease, 0.18 year) (Figure 1).
Figure 1. Change in the Disparity in Life Expectancy If Selected Diseases Were Eliminated.
The change in the disparity in life expectancy according to educational level is calculated as (LEless educated – LEmore educated) – (LEless educated – LEmore educated when the cause-specific risk of death is set to zero), with LE denoting life expectancy until 75 years of age.
The change in the disparity in life expectancy according to race is calculated as (LEblacks – LEwhites)–(LEblacks – LEwhites when the cause-specific risk of death is set to zero), with LE denoting life expectancy until 75 years of age.
When categorized into six groups, education level appeared tohave some dose–response effect on mortality. As comparedwith persons with less than a ninth-grade education, the lifeexpectancy was 7.4 years greater for those with some high-schooleducation, 13.1 years greater for those who graduated from highschool, 12.4 years greater for those with some college education,13.0 years greater for college graduates, and 12.8 years greaterfor those with a graduate-level education.
The disparity in life expectancy according to race (up to 75years of age) was 1.80 years overall (1.46 years among less-educatedpersons and 1.94 years among more-educated persons). The eliminationof hypertension would have the biggest effect, decreasing thisdisparity to 1.57 years (a decrease of 0.23 year per person)(Figure 1). HIV remained the second most important cause ofthe disparity (a decrease of 0.18 year per person), followedby homicide (decrease, 0.13 year), diabetes (decrease, 0.12year), colon cancer (decrease, 0.08 year), pneumonia (decrease,0.08 year), and ischemic heart disease (decrease, 0.06 year).
Potential life-years lost and gains in life expectancy producesimilar rankings of the importance of specific diseases to thedisparities in mortality. Thus, the results in the analysisof potential life-years lost appear robust against the problemof competing risks.
Marginal Effect of Education and Race
Adjustment for race had a minimal effect on the educationaldisparity in potential life-years lost (Figure 2). For deathfrom all causes, the disparity was 9.3 years per person (99percent confidence interval, 8.7 to 10.7) without adjustmentfor race, and 9.2 years per person (99 percent confidence interval,8.5 to 10.7) with such adjustment. The educational disparityin potential life-years lost was statistically significant forevery specific cause of death except renal and rheumatologicdiseases, regardless of adjustment for race.
Figure 2. The Effect of Adjustment for Race on the Disparity in Potential Life-Years Lost between Persons with Less Education and Those with More Education, According to the Specific Cause of Death.
Potential life-years lost are calculated as years of potential life lost before 75 years of age. Trauma includes deaths from accidents, suicides, and homicides. The I bars represent 99 percent confidence intervals.
For all causes, the racial disparity was 2.9 potential life-yearslost per person (99 percent confidence interval, 2.4 to 3.9)without adjustment for education and 1.8 per person (99 percentconfidence interval, 1.4 to 2.8) with such adjustment. Irrespectiveof adjustments for education, blacks lost significantly morepotential life-years than whites due to cardiovascular disease,infections, diabetes, liver disease, and trauma (Figure 3).Adjustment for education had the greatest effect on racial disparitiesin deaths from cardiovascular disease and cancer, the two causesfor which the educational disparity was largest.
Figure 3. The Effect of Adjustment for Level of Education on the Disparity in Potential Life-Years Lost between Black Persons and White Persons, According to the Specific Cause of Death.
Trauma includes deaths from accidents, suicides, and homicides. The I bars represent 99 percent confidence intervals.
Adjustment for Recent Trends in Mortality from HIV Disease
After protease inhibitors were introduced in 1996, mortalityfrom HIV disease declined dramatically.22 When we adjusted forthis trend, the contribution of HIV to the racial disparityin potential life-years lost was 11.2 percent. Before this trend(with mortality estimates from the 1994 National Health InterviewSurvey), HIV contributed 17.4 percent to the total racial disparityin potential life-years lost. Recent improvements in mortalityfrom HIV have favored whites more than blacks. Had mortalityfrom HIV disease improved equally, the racial disparity in mortalityfrom HIV disease would have dropped further, contributing only6.5 percent.
Discussion
Though numerous studies have found racial and educational differencesin mortality, only a small number have attempted to identifywhich diseases contribute most to existing disparities.11,14,15,30,31,32,33,34,35These studies have been limited in several ways. First, almostall11,14,15,31,32,34 compared mortality risk ratios, which failto reflect absolute numbers of deaths or age at death. For example,the relative risk of death from prostate cancer for blacks ascompared with whites is particularly large,34 which suggeststhat it contributes substantially to the racial disparity inlife expectancy. However, we estimate that this cause contributesonly 3.3 percent to the total disparity in potential life-yearslost — mainly because death from prostate cancer tendsto occur late in life and, to a lesser extent, because it isa relatively uncommon cause of death. Previous studies are alsolimited because they examined data collected before the HIVepidemic,30 examined data from outside the United States,11,31,33examined racial or educational disparities but not both,35 andlacked sufficient details about specific causes of death.35
Our study provides important information for policy makers,researchers, and clinicians. So far, much of the research attemptingto understand health disparities has focused on ischemic heartdisease. A recent review of the literature found 63 studiesthat examined racial differences in the use of cardiovascularprocedures.36 Though significant disparities in the use of coronaryangiography, angioplasty, and bypass surgery are evident, thebroader implications of these findings for directing futureresearch and interventions must be considered. Ischemic heartdisease contributes only 5.5 percent to the total racial disparityin potential life-years lost. HIV disease and hypertension eachcontribute two to three times as much.
Though we have not examined factors that might explain thesedisparities, such as health insurance, access to care, qualityof care, or health-related behavior, our results indicate areasthat warrant the investment of greater resources. The top sixcontributors to the educational disparity in mortality are ischemicheart disease, lung cancer, stroke, pneumonia, congestive heartfailure, and lung disease, which together contribute 40.4 percentto the total disparity according to educational level in potentiallife-years lost. All six are smoking-related diseases, suggestingthat interventions to prevent smoking could have an enormousimpact. Also, future research and interventions should targetscreening and treatment for hypertension and prevention andtreatment of HIV infection among blacks. Improvements in thedelivery of cardiac-revascularization procedures would havea much smaller effect on racial disparities.
Disentangling the effects of race and education on health isoften challenging. A few observations deserve mention. First,the disparity in life expectancy when it is truncated at 75years of age is greater according to level of education thanaccording to race (9.2 and 1.8 years, respectively). These estimateswere truncated at 75 years so they would be comparable to theestimates of potential life-years lost. Without any age cutoff,however, the disparities in life expectancy according to levelof education and race are 4.9 and 6.3 years, respectively, whichare similar to values reported in previous studies1,2 and suggestthat much of the racial disparity occurs after the age of 75.Regardless of which cutoff is used, the relative importanceof different causes of death to the overall disparity in mortalityremains the same for both educational and racial disparities.
Second, the level of education and race each appear to havestrong, independent effects that persist after adjustment forthe other. Third, the patterns of racial and educational disparityare markedly different, which suggests that different sets offactors may explain these patterns. As previously mentioned,smoking-related diseases are more strongly associated with levelof education than with race. We did not examine cigarette use;however, the results are consistent with studies showing thatrates of smoking are higher among less-educated persons butvary less according to race.37,38,39
Our study assumes that dying from one disease is noninformativeabout the risk of death from another. Since violation of thisassumption is more likely when two diseases with similar riskfactors (i.e., correlated competing hazards) are examined, conclusionsabout stroke as compared with ischemic heart disease, for example,require some caution. Less concern is warranted for unrelateddiseases, such as HIV and cancer,40 and also when patterns ofmortality are compared according to race and level of education.
In addition, death certificates may inaccurately record thetrue cause of death, and the underlying cause-of-death codingcould be biased. The person filling out the death certificatedoes not determine the underlying cause. Instead, the NationalCenter for Health Statistics follows a widely used coding algorithm,which may reduce the potential for bias. Misclassification maystill occur, however, and the study results should be viewedwith this potential limitation in mind.
Given limited resources to eliminate health disparities, weneed to focus our efforts so as to achieve the maximal gain.Our data suggest that targeting ischemic heart disease and lungcancer would be most useful in reducing the educational disparityin mortality, whereas targeting hypertension, HIV, trauma, anddiabetes would have the greatest effect on the racial disparity.
Supported by an Institutional National Research Service Awardfrom the Health Resources and Services Administration (6 T32PE-190001-13 R1), the Mary and Irving Lazar Program in HealthServices Research, University of California at Los Angeles Departmentof Medicine Specialty Training and Advanced Research program,a grant (5PO1 HS10858) from the Excellence Centers to EliminateEthnic/Racial Disparities Program of the Agency for Health CareResearch and Quality, and a grant from the Behavioral and SocialResearch Program at the National Institute on Aging to the Centeron Biodemography and Population Health of the University ofCalifornia at Los Angeles and the University of Southern California(P30-AG17265).
Presented in part at the 24th Annual National Meeting of theSociety of General Internal Medicine, San Diego, Calif., May2–5, 2001.
We are indebted to Hsin-ju Hsieh for her programming assistance.
Source Information
From the Division of General Internal Medicine and Health Services Research (M.D.W., M.F.S., S.L.E.) and the School of Public Health, Departments of Health Services (M.F.S., S.L.E.) and Biostatistics (W.J.B.), University of California at Los Angeles, Los Angeles.
Address reprint requests to Dr. Wong at the University of California at Los Angeles, Division of General Internal Medicine and Health Services Research, 911 Broxton Plaza, Los Angeles, CA 90095-1736, or at miwong{at}mednet.ucla.edu.
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$20,000) and the poverty-line cutoff, which is based on family income adjusted for household size. Results for income were unaffected by the use of other cutoffs and were similar to the disparities according to level of education. 
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