Presentation from the 2016 International Conference on Aging in the Americas hosted at the University of Texas at San Antonio Downtown Campus, Sept. 14-16.

1. Background Goal Data & Measures Methods Results Conclusion
Links between Occupational History and
Functional Limitations among Older Adults
in Mexico
Hiram Beltr´an-S´anchez, UCLA
Anne R. Pebley, UCLA
Noreen Goldman, Princeton
.
International Conference of Aging in the Americas (ICAA)
September 14-16, 2016
San Antonio, Texas with U.T Austin

2. Background Goal Data & Measures Methods Results Conclusion
Background
1 Work and Health
Occupation itself defines status in society
Occupational ranking ùñ level of psychosocial stressors
Work ùñ level of physical strain/injury/environmental hazards
on the job

3. Background Goal Data & Measures Methods Results Conclusion
Background
1 Work and Health
Occupation itself defines status in society
Occupational ranking ùñ level of psychosocial stressors
Work ùñ level of physical strain/injury/environmental hazards
on the job
2 Other Causal and Non-Causal Pathways
links between health and job characteristics
higher risk of health problems, disability & mobility limitations
throughout life ùñ early life/family’s low SES/low social
mobility

4. Background Goal Data & Measures Methods Results Conclusion
Background
1 Work and Health
Occupation itself defines status in society
Occupational ranking ùñ level of psychosocial stressors
Work ùñ level of physical strain/injury/environmental hazards
on the job
2 Other Causal and Non-Causal Pathways
links between health and job characteristics
higher risk of health problems, disability & mobility limitations
throughout life ùñ early life/family’s low SES/low social
mobility
3 Disability Gradients at Older Ages in Mexico
significant social gradients in the number of physical activity
limitations in MHAS (Smith & Goldman 2007)
The above is particularly true in Urban areas in Mexico

5. Background Goal Data & Measures Methods Results Conclusion
Background
Importantly:
Most work looking at SES gradients in
health or functioning examines education
and/or income
There is little work on occupation and
health, and the relevant work tends to
focus on occupational safety

6. Background Goal Data & Measures Methods Results Conclusion
Objective:
Assess the degree to which social gradients in
mobility limitations (by educational
attainment and net worth) are associated with
work at older ages in Mexico

7. Background Goal Data & Measures Methods Results Conclusion
Objective:
Assess the degree to which social gradients in
mobility limitations (by educational
attainment and net worth) are associated with
work at older ages in Mexico
1 Determine whether occupational
differences account for a significant portion
of the social gradient in mobility limitations
2 Assess which occupations are associated
with the highest rates of mobility
limitations at older ages for the entire
sample, and for men and women

8. Background Goal Data & Measures Methods Results Conclusion
Data & Measures
MHAS: Mexican Health & Aging Study, 2001
We study 12,419 people aged 50+ in 2001
Dependent variable :
Mobility limitations, ranging from 0 to 18:
Mobility limitation MHAS response Code
Walking
Sitting
Stairs 1. Yes 0= No difficulty
Stooping 2. No 1= Difficulty
Reaching 6-7. Can’t do/doesn’t do 2= Unable
Lifting/carrying heavy weights
Lifting/carrying up to 10 pounds
Grasping

9. Background Goal Data & Measures Methods Results Conclusion
Data & Measures
Independent variables :
Years of education: primary SES measure
Net worth: value of all assets minus debts
for individuals or for the couple if
married/cohabiting
Main occupation during adulthood:
For the following question, please think about the activities that
you performed in your main job throughout your life: What is the
name of the job, profession, post, or position you held in your main
job?

10. Background Goal Data & Measures Methods Results Conclusion
Methods
Hurdle model : a two-part model
1 for the probability of having any mobility
limitation (unconditional), and
2 for the number of limitations given that at
least 1 limitation has been reported
(conditional).
Formal approach:
PrpY yq
#
Prpy 0q ùñ Logistic model (part 1)
Prpy|y ¡ 0q ùñ Truncated-at-zero Neg Bin (part 2)

11. Background Goal Data Measures Methods Results Conclusion
Results: Total Population
Hurdle model (part 1): Modelling the probability of having
any mobility limitation
Gross effect Net effect
SES indicator Mod1 Mod1+occ Mod2 Mod2+occ Mod3 Mod3+occ
Education (years) -0.059*** -0.053*** -0.056*** -0.051***
Net worth (ref= deciles 1-5)
deciles 6-9 -0.171*** -0.109** -0.090* -0.078
decile 10 -0.345*** -0.188** -0.078 -0.066
¦¦¦p 0.001, ¦¦p 0.01, ¦p 0.05, controlling for age, age-squared, and sex

12. Background Goal Data Measures Methods Results Conclusion
Results: Total Population
Hurdle model (part 1): Modelling the probability of having
any mobility limitation
Gross effect Net effect
SES indicator Mod1 Mod1+occ Mod2 Mod2+occ Mod3 Mod3+occ
Education (years) -0.059*** -0.053*** -0.056*** -0.051***
Net worth (ref= deciles 1-5)
deciles 6-9 -0.171*** -0.109** -0.090* -0.078
decile 10 -0.345*** -0.188** -0.078 -0.066
¦¦¦p 0.001, ¦¦p 0.01, ¦p 0.05, controlling for age, age-squared, and sex
Change in the magnitude of SES coefficients (%)
SES indicator Gross effect Net effect
Education (years) -10.2 -8.9
Net worth (ref= deciles 1-5)
deciles 6-9 -36.3 -13.3
decile 10 -45.5 -15.4

13. Background Goal Data Measures Methods Results Conclusion
Results: Total Population
Hurdle model (part 2): Modelling the number of limitations
given that at least 1 limitation has been reported
Gross effect Net effect
SES indicator Mod1 Mod1+occ Mod2 Mod2+occ Mod3 Mod3+occ
Education (years) -0.017*** -0.013*** -0.014*** -0.011***
Net worth (ref= deciles 1-5)
deciles 6-9 -0.062*** -0.047* -0.046* -0.041*
decile 10 -0.162*** -0.120*** -0.109** -0.099**
¦¦¦p 0.001, ¦¦p 0.01, ¦p 0.05, controlling for age, age-squared, and sex
Change in the magnitude of SES coefficients (%)
SES indicator Gross effect Net effect
Education (years) -23.5 -21.4
Net worth (ref= deciles 1-5)
deciles 6-9 -24.2 -10.9
decile 10 -25.9 -9.2

14. Predicted OVERALL MEAN number of mobility limitations
Total Population (top 5 occupations)
Artisans Workers in Prod of Textiles
Leather Products related goods
Agricultural Workers
No−occupation
Workers in the Making of Foods
Beverages Tobacco Prod
Domestic Workers
0 1 2 3

15. Predicted OVERALL MEAN number of mobility limitations
Males (top 5 occupations)
Artisans Workers in Prod
Repair Maintenance
Drivers Assistant Drivers of
Motorized Surface Transport
Agriculture, Livestock
Forestry Fishing
Workers in the Making of Foods
Beverages Tobacco Prod
Agricultural Workers
0 1 2 3

16. Predicted OVERALL MEAN number of mobility limitations
Females (top 5 occupations)
Workers in Service Industry
Assistants, Laborers, etc. in Industrial
Production, Repair Maintenance
Domestic Workers
Agricultural Workers
Workers in the Making of Foods
Beverages Tobacco Prod
0 1 2 3

17. Background Goal Data Measures Methods Results Conclusion
Conclusion:
Occupational differences account for a
sizeable portion of the social gradient in
mobility limitations

18. Background Goal Data Measures Methods Results Conclusion
Conclusion:
Occupational differences account for a
sizeable portion of the social gradient in
mobility limitations
20% reduction in the magnitude of the coefficient in
the educational gradient (total pop)
10% reduction in the magnitude of the coefficient in
the net worth gradient (total pop)

19. Background Goal Data Measures Methods Results Conclusion
Conclusion:
Physically demanding occupations are
associated with the highest rates of
mobility limitations at older ages

20. Background Goal Data Measures Methods Results Conclusion
Conclusion:
Physically demanding occupations are
associated with the highest rates of
mobility limitations at older ages
The following occupations fare the worst
Men: Agricultural workers, and merchants/sales
representatives
Women: Workers in the Making of Foods, Beverages
Tobacco Products, and agricultural workers

21. Background Goal Data Measures Methods Results Conclusion
beltrans@ucla.edu
Thank you!