1. Non-Medical Determinants of Health Victor R. Fuchs Stanford University and National Bureau of Economic Research © 2002 by Victor R. Fuchs. All rights reserved.

2. A Taxonomy of Non-Medical Determinants Genes Physical environment –Womb –Home –Workplace –Water and air –Streets and highways Psycho-social environment –Home –School –Media –Workplace –Community (continued)

3. A Taxonomy, cont. Socio-economic factors –Income –Education –Ethnicity Individual behaviors –Cigarettes –Alcohol abuse –Diet –Exercise –Illegal drugs –Sexual practices Interactions

4. How Important are the Non-Medical Determinants? Compared with medical care? Compared with one another?

5. 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 –When? –Where? –What? –Who?

6. Life Expectancy at Birth, 149 Countries in late 1990s, Averages by Decile of Real GDP per Capita 5001,0005,00010,00030,0002,000 Years 00

7. 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 5001,0005,00010,00030,0002,000 1900-29 1930s 1960s 1990s Years World USA 00

8. Sex Ratio of Life Expectancy, 149 Countries in late 1990s, Averages by Decile of Real GDP per Capita 5001,0005,00010,00030,0002,000 Women : men ratio

9. Sex Ratio of Life Expectancy, 149 Countries in late 1990s, Averages by Decile of Real GDP per Capita plus Same Variables for U.S. Decade Averages Since 1900 5001,0005,00010,00030,0002,000 1900-29 1930s 1970s 1990s Women : men ratio World USA

10. Neo-natal and Post Neo-natal Mortality, U.S. 1999 0 2 4 6 8 10 BlackAmerican Indian WhiteHispanicChinese Neo-natal Post neo-natal Deaths per 1,000 live births

11. Predicted a Probability of Smoking at Age 24 by Years of Schooling Completed, White, Non-Hispanic Men in Central California Cohort born 1936-46 0.0 0.2 0.4 0.6 0.8 1.0 121518 Years of schooling p Cohort born 1947-55 0.0 0.2 0.4 0.6 0.8 1.0 121518 Years of schooling p

12. Predicted a Probability of Smoking at Age 24 by Years of Schooling Completed and at Age 17 by Years of Schooling That Will Be Completed, White, Non- Hispanic Men in Central California Cohort born 1936-46 0.0 0.2 0.4 0.6 0.8 1.0 121518 Years of schooling p Age 17 Age 24 Cohort born 1947-55 0.0 0.2 0.4 0.6 0.8 1.0 121518 Years of schooling p Age 17 Age 24

13. 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

14. 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 1.00 1.05 1.10 1.15 1.20 1.25 1.30 All causes Respiratory Cardio- vascular Lung cancer Other mal. neo. Cerebro- vascular PollutionCigarettesObesity Ratio

15. 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 1.00 1.05 1.10 1.15 1.20 1.25 1.30 TotalInpatientOut-patientMed.Surg.Resp. PollutionCigarettesObesity Ratio

16. 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