Transcript How Important are the Non

Non-Medical
Determinants of Health
Victor R. Fuchs
Stanford University
and
National Bureau of Economic Research
© 2002 by Victor R. Fuchs. All rights reserved.
A Taxonomy of Non-Medical Determinants
• Genes
• Physical environment
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Womb
Home
Workplace
Water and air
Streets and highways
• Psycho-social environment
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Home
School
Media
Workplace
Community
(continued)
A Taxonomy, cont.
• Socio-economic factors
– Income
– Education
– Ethnicity
• Individual behaviors
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Cigarettes
Alcohol abuse
Diet
Exercise
Illegal drugs
Sexual practices
• Interactions
How Important are the Non-Medical
Determinants?
• Compared with medical care?
• Compared with one another?
How Important are the Non-Medical
Determinants?
• Compared with medical care?
• Compared with one another?
Depends on Perspective
– Changes over time
– Differences at a given time
Depends on Context
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When?
Where?
What?
Who?
Life Expectancy at Birth, 149 Countries in late 1990s,
Averages by Decile of Real GDP per Capita
Years
80
80
70
70
60
60
50
50
40
40
30
0
30
0
500
1,000
2,000
5,000
10,000
30,000
GDP per Capita, 1999 U.S. dollars (logarithmic scale)
Life Expectancy at Birth, 149 Countries in late 1990s,
Averages by Decile of Real GDP per Capita plus Same
Variables for U.S. Decade Averages Since 1900
Years
80
1990s
World
80
USA
70
70
1930s
60
1960s
1900-29
50
60
50
40
40
0
30
0
30
500
1,000
2,000
5,000
10,000
30,000
GDP per Capita, 1999 U.S. dollars (logarithmic scale)
Sex Ratio of Life Expectancy, 149 Countries in late
1990s, Averages by Decile of Real GDP per Capita
Women : men ratio
1.14
1.14
1.12
1.12
1.10
1.10
1.08
1.08
1.06
1.06
1.04
1.04
1.02
1.02
1.00
1.00
500
1,000
2,000
5,000
10,000
30,000
GDP per Capita, 1999 U.S. dollars (logarithmic scale)
Sex Ratio of Life Expectancy, 149 Countries in late
1990s,
Averages by Decile of Real GDP per Capita plus Same
for U.S. Decade Averages Since 1900
Women Variables
: men ratio
1.14
1.14
World
1.12
1.10
1.12
1970s
1990s
1.10
1.08
1.08
USA
1900-29
1.06
1.06
1930s
1.04
1.04
1.02
1.02
1.00
1.00
500
1,000
2,000
5,000
10,000
30,000
GDP per Capita, 1999 U.S. dollars (logarithmic scale)
Neo-natal and Post Neo-natal Mortality,
U.S. 1999
Deaths per 1,000 live births
10
Neo-natal
Post neo-natal
8
6
4
2
0
Black
American
Indian
White
Hispanic
Chinese
Predicteda Probability of Smoking at Age 24
by Years of Schooling Completed,
White, Non-Hispanic Men in Central California
p
Cohort born 1936-46
p
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
12
15
18
Years of schooling
Cohort born 1947-55
12
15
18
Years of schooling
Predicteda Probability of Smoking at Age 24 by Years
of Schooling Completed and at Age 17 by Years of
Schooling That Will Be Completed, White, NonHispanic Men in Central California
p
Cohort born 1936-46
1.0
Age 17
Age 24
0.8
p
1.0
0.6
0.4
0.4
0.2
0.2
0.0
0.0
15
Years of schooling
18
Age 17
Age 24
0.8
0.6
12
Cohort born 1947-55
12
15
18
Years of schooling
Annual Rate of Change in Age-Adjusted Mortality,by
Sex, Lung Cancer and Other Malignant Neoplasms
(Five year moving average centered on middle year)
Percent change per annum
8
Lung, men
Other, men
Lung, women
Other, women
6
4
2
0
-2
1963
1968
1973
1978
1983
1988
1993
Ratio of Predicted Mortality of Whites Ages 65-84 in
the Worst 10 Percent of Areas to the Best 10 Percent
by Risk Factor, Controlling for Other Variables*, for
137
MSAs
>
100,000,
1989-91
Ratio
1.30
Pollution
Cigarettes
Obesity
1.25
1.20
1.15
1.10
1.05
1.00
Respiratory CardioAll causes
vascular
Lung
Other mal. Cerebroneo.
vascular
cancer
Ratio of Predicted Medical Care Utilization of Whites
Ages 65-84 in the Worst 10 Percent of Areas to the Best
10 Percent by Risk Factor, Controlling for Other
Variables*, 183 MSAs > 100,000, 1989-91
Ratio
1.30
Pollution
Cigarettes
Obesity
1.25
1.20
1.15
1.10
1.05
1.00
Total
Inpatient Out-patient
Med.
Surg.
Resp.
Summary
• Non-medical determinants are numerous,
varied, and sometimes very important
• Their importance, relative to medical care
and relative to one another, depends greatly
on perspective and context
• We need a much firmer understanding of the
health effects of non-medical determinants
and their interactions
• When definitive critical experiments are not
feasible, we need to seek understanding with
a wide variety of methodologies and data sets