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Education, income inequality, and mortality: a multiple regression analysis

BMJ 2002; 324 doi: https://doi.org/10.1136/bmj.324.7328.23 (Published 05 January 2002) Cite this as: BMJ 2002;324:23

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WHY IS EDUCATION LESS IMPORTANT AT THE INDIVIDUAL LEVEL OF ANALYSIS THAN AT THE STATE LEVEL?

Blakely and Kawachi show in a new analysis of self-reported health
that education measured at the level of individuals does not fully account
for income inequality measured at the state level. Lack of high school
education completely accounted for the income inequality effect in my
study of U.S. states. The discrepancy in findings is not surprising since
our analyses are at different levels of aggregation, use different methods
of analysis, and measure different outcomes among other differences.

More specifically, my paper points out that the large education-
mortality effect in part reflects other factors than education. [1 p3] I
briefly mention the results of an expanded regression analysis that
controls for additional confounders: (1) percent of persons 15 years old
and older married, (2) percent of employed persons 16 years and older in
high injury risk occupations, (3) percent of persons 18 years and older
current smokers, (4) percent of persons without health insurance, (5)
percent African American, or Latino population, and (6) percent of
population foreign born. The additional control variables improve model
fit significantly (R2 adj. = .82) and reduce the direct effect of
education by 40%. However, the education effect remains significant (b =
.066; p = .01) while the income inequality effect continues to be spurious
(b = .54; p = .89). The more detailed analysis was submitted to BMJ, but
not published. The results of the expanded regression analysis are
available upon request.

Blakely and Kawachi’s new data provide only weak support for the
causal contribution of income inequality when education is added as
control. The odds ratios comparisons (model 2) indicate that five out of
eight comparisons are statistically insignificant. In addition, exposure
to medium levels of income inequality results in poorer self-rated health
than exposure to high levels of income inequality. This is an unexpected
result that suggests that some confounding variables may be at work. My
question is: will the income inequality effect disappear with a more
complete model specification? My guess is that it will be further reduced
and the education effect will stay intact. This result would also be
consistent with studies of individuals that found education to be an
important predictor of mortality. [2 3 4]

REFERENCES

1. Muller A. Education, income inequality, and mortality: a multiple
regression analysis. BMJ 2002;324:1-4.

2. Backlund E, Sorlie PD, Johnson NJ. A comparison of the
relationships of education and income with mortality: the national
longitudinal mortality study. Soc Sci Med 1999;49:1373-84.

3. Elo IT, Preston SH. Educational differentials in mortality: United
States, 1979-85. Soc Sci Med 1996;42:47-57.

4. Hoyert DL, Arias E, Smith BL, Murphy SL, Kochanek KD. Deaths:
Final data for 1999. National Vital Statistics Reports, vol. 49 no
8.Hyattsville, Maryland: National Center for Health Statistics. 2001.

Competing interests: No competing interests

07 April 2002
Andreas Muller
Professor, Health Services Administration
University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock AR 72204