eAppendix: Supplemental Material
This supplement includes additional references relevant to the points made and additional
figures that support and clarify the conclusions.
Additional References
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S3. Centers for Disease Control and Prevention (CDC). Annual smoking-attributable
mortality, years of potential life lost, and economic costs--United States, 1995-1999.
MMWR Morb Mortal Wkly Rep. 2002 Apr 12;51(14):300-3.
S4. Giesinger I, Goldblatt P, Howden-Chapman P, Marmot M, Kuh D, Brunner E
Association of socioeconomic position with smoking and mortality: the contribution of
early life circumstances in the 1946 birth cohort. J Epidemiol Community Health. 2014
Mar 1;68(3):275-9.
S5. Singh GK, Siahpush M. Widening rural-urban disparities in life expectancy, U.S..,
1969-2009. Am J Prev Med. 2014 Feb;46(2):e19-29.
S6. Vermeulen R, Silverman DT, Garshick E, Vlaanderen J, Portengen L, Steenland K.
Exposure-Response Estimates for Diesel Engine Exhaust and Lung Cancer Mortality
Based on Data from Three Occupational Cohorts. Environ Health Perspect. 2014
122:172-177.
S7. Guo P, Yokoyama K, Suenaga M, Kida H. Mortality and life expectancy of
Yokkaichi asthma patients, Japan: late effects of air pollution in 1960-70s. Environ
Health.2008;7:8.
S8. Pope CA 3rd, Ezzati M, Dockery DW. Fine-particulate air pollution and life
expectancy in the United States. N Engl J Med. 2009; 360:376-386. Also, Lipfert FW.
Air pollution and life expectancy. N Engl J Med. 2009;360:2033 and author reply 20332044.
S9. Chen Y, Ebenstein A, Greenstone M, Li H. Evidence on the impact of sustained
exposure to air pollution on life expectancy from China's Huai River policy. Proc Natl
Acad Sci U S A. 2013;110:12936-12941.
S10. Escobedo LG, Peddicord JP. Smoking prevalence in US birth cohorts: the influence
of gender and education. Am J Public Health. 1996 Feb;86(2):231-6.
S11 Guo Y, Li S, Tian Z, Pan X, Zhang J, Williams G. The burden of air pollution on
years of life lost in Beijing, China, 2004-08: retrospective regression analysis of daily
deaths. BMJ. 2013 Dec 9;347:f17139
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and all-cause mortality after restrictions on sulphur content of fuel in Hong Kong: an
intervention study. Lancet. 2002 Nov 23;360(9346):1646-52.
S13. Murray CJ, Nelson CR. State-space modeling of the relationship between air quality
and mortality. J Air Waste Manag Assoc. 2000;50:1075-1080.
S14. Murray CJ, Lipfert FW. Inferring frail life expectancies in Chicago from daily
fluctuations in elderly mortality. Inhal Toxicol. 2013;25:461-479.
S15. Knudsen SJ, Air pollution data for Toronto, Canada: A frail population state-space
model, in New Trends in Statistical Modelling, Proc 16th Int Workshop on Statistical
Modelling, Odense, Denmark, July 2-6, 2001. B Klein and L Korsholm, editors.
S16 Holford TR, Meza R, Warner KE, Meernik C, Jeon J, Moolgavkar SH, Levy DT.
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Additional Figures
Figure S1. Trends in county-level life expectancies are compared, showing similar
dips ca. 1985-90 in New York, San Francisco, and New Orleans that may relate to
the AIDS epidemic. This dip is not seen in Salt Lake City or Minneapolis, which
may have had different lifestyles. This anomaly complicates cross-county
comparisons and shows the influence of non-environmental factors. The range in
life expectancies among these counties is 6-8 y.
Figure S2. The Harvard Six Cities Cohort Study began in the mid-1970s and has
figured prominently in the air pollution – health effects literature. However, life
expectancy calculations are limited to populations and cannot readily be computed
for a mixed-age cohort; life expectancies for the six relevant counties are shown
above. The relatively “clean” counties for Watertown and Topeka show steadily
increasing life expectancies, which level off after about 1985 in the more polluted
counties and in Portage, WI. The lower values in St. Louis may be due to its racial
mix. All counties show steady increases in the 1970s, when the Clean Air Act took
effect, regardless of their individual pollution levels. After about 1980, the relative
rankings among cities vary. The range in life expectancies is 8-10 y.
.
Figure S3. There is a close correlation between mean life expectancies at birth and
at age 65 (R=0.98, slope = 0.65 [sdev=0.03]). Thus, regression coefficients with
respect to life expectancies at birth may be converted to those at age 65 by
multiplying by 0.65. The intercept of this regression is 49; thus life expectancy at
birth would be 49 when life expectancy at age 65 is 0. When life expectancy at birth
is 100, life expectancy at age 65would be 32, so that the predicted mean attained age
would be 98, which is reasonable agreement.
Figure S4 The standard deviation of life expectancies for the 3116 counties is a
measure of their diversity. For life expectancy at birth, it reaches a minimum about
1983 and then increased back to its former level. The change in 99% confidence
intervals is from about 10 –12 y. This suggests that counties tend become slightly
less diverse and then become more diverse in later years because life expectancies
improved greatly in some counties and leveled off in others. Initial indications are
that this increase continues in subsequent years..
Figure S5. Life expectancies for males increase monotonically with decreased
smoking but level off for females. The curves for males and females do not converge
for either no-smokers or all-smokers, suggesting effects of other correlated
variables. They imply an average loss of about 20 y per smoker, which is greater
than has reported from direct studies by smoking habit. The difference between
males and females is 5-6 y .
Figure S6. Relationships between life expectancy at age 65 and prevalence of
smoking (shown as the percentage of non-smokers) are linear for males but tend to
level off for females, similarly to Figure S5. Life expectancies for the two genders
appear to converge at about 8 y for all-smokers and 19 y for no smokers. Thie
regressions indicates a loss of about 9 y per smoker, even after age 65. However,
these comparisons do not account for former smokers or the duration of smoking.
It is also possible that other correlated behaviors contribute to the loss of life
expectancy.
Figure S7. Life expectancies by state are strongly correlated with adult smoking
prevalence in that state. Possible outliers are District of Columbia, which is 100%
urban, and Utah is proscribed by religion as are other lifestyle choices. The
correlations are 0.91 and 0.81 for birth and age 65 respectively. The slopes are –
0.24 and –0.14 and are similar to the temporal slopes (-0.21,-0.09), which lends
credence to both. However, these cross-state relationships are not matched by
gender or time period.
Figure S8. Holford et al. (2014) show a linear decrease in smoking prevalence since 1960. Other
data show peak smoking rates in the mid-1940s, perhaps because of military service and
aggressive marketing. The mean rate of smoking has dropped by about 50% since the 1960s.