links between occupational history and functional limitations among older adults in mexico - hiram...
TRANSCRIPT
Background Goal Data & Measures Methods Results Conclusion
Links between Occupational History andFunctional Limitations among Older Adults
in Mexico
Hiram Beltran-Sanchez, UCLAAnne 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
Background Goal Data & Measures Methods Results Conclusion
Background1 Work and Health
Occupation itself defines status in societyOccupational ranking ùñ level of psychosocial stressorsWork ùñ level of physical strain/injury/environmental hazards
on the job
2 Other Causal and Non-Causal Pathwayslinks between health and job characteristicshigher risk of health problems, disability & mobility limitations
throughout life ùñ early life/family’s low SES/low socialmobility
3 Disability Gradients at Older Ages in Mexicosignificant social gradients in the number of physical activity
limitations in MHAS (Smith & Goldman 2007)The above is particularly true in Urban areas in Mexico
Background Goal Data & Measures Methods Results Conclusion
Background1 Work and Health
Occupation itself defines status in societyOccupational ranking ùñ level of psychosocial stressorsWork ùñ level of physical strain/injury/environmental hazards
on the job2 Other Causal and Non-Causal Pathways
links between health and job characteristicshigher risk of health problems, disability & mobility limitations
throughout life ùñ early life/family’s low SES/low socialmobility
3 Disability Gradients at Older Ages in Mexicosignificant social gradients in the number of physical activity
limitations in MHAS (Smith & Goldman 2007)The above is particularly true in Urban areas in Mexico
Background Goal Data & Measures Methods Results Conclusion
Background1 Work and Health
Occupation itself defines status in societyOccupational ranking ùñ level of psychosocial stressorsWork ùñ level of physical strain/injury/environmental hazards
on the job2 Other Causal and Non-Causal Pathways
links between health and job characteristicshigher risk of health problems, disability & mobility limitations
throughout life ùñ early life/family’s low SES/low socialmobility
3 Disability Gradients at Older Ages in Mexicosignificant social gradients in the number of physical activity
limitations in MHAS (Smith & Goldman 2007)The above is particularly true in Urban areas in Mexico
Background Goal Data & Measures Methods Results Conclusion
Background
Importantly:
Most work looking at SES gradients inhealth or functioning examines educationand/or income
There is little work on occupation andhealth, and the relevant work tends tofocus on occupational safety
Background Goal Data & Measures Methods Results Conclusion
Objective:
Assess the degree to which social gradients inmobility limitations (by educationalattainment and net worth) are associated withwork at older ages in Mexico
1 Determine whether occupationaldifferences account for a significant portionof the social gradient in mobility limitations
2 Assess which occupations are associatedwith the highest rates of mobilitylimitations at older ages for the entiresample, and for men and women
Background Goal Data & Measures Methods Results Conclusion
Objective:
Assess the degree to which social gradients inmobility limitations (by educationalattainment and net worth) are associated withwork at older ages in Mexico
1 Determine whether occupationaldifferences account for a significant portionof the social gradient in mobility limitations
2 Assess which occupations are associatedwith the highest rates of mobilitylimitations at older ages for the entiresample, and for men and women
Background Goal Data & Measures Methods Results Conclusion
Data & Measures
MHAS: Mexican Health & Aging Study, 2001We study 12,419 people aged 50+ in 2001Dependent variable :
Mobility limitations, ranging from 0 to 18:Mobility limitation MHAS response Code
WalkingSittingStairs 1. Yes 0= No difficulty Stooping 2. No 1= Difficulty Reaching 6-7. Can’t do/doesn’t do 2= UnableLifting/carrying heavy weightsLifting/carrying up to 10 poundsGrasping
Long & Pavalko MHAS code Codewalkingsittingstairs 1. Yes 0=no difficulty stooping 2. No 1=difficulty reaching 6-7. Can’t do/doesn’t do 2=unablelifting/carrying heavy weightslifting/carrying up to 10 poundsgraspingstanding
Background Goal Data & Measures Methods Results Conclusion
Data & Measures
Independent variables :
Years of education: primary SES measure
Net worth: value of all assets minus debtsfor individuals or for the couple ifmarried/cohabiting
Main occupation during adulthood:
For the following question, please think about the activities thatyou performed in your main job throughout your life: What is thename of the job, profession, post, or position you held in your mainjob?
Background Goal Data & Measures Methods Results Conclusion
Methods
Hurdle model : a two-part model1 for the probability of having any mobility
limitation (unconditional), and2 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)
Background Goal Data & Measures Methods Results Conclusion
Results: Total Population
Hurdle model (part 1): Modelling the probability of havingany 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* -0.078decile 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 -8.9Net worth (ref= deciles 1-5)deciles 6-9 -36.3 -13.3decile 10 -45.5 -15.4
Background Goal Data & Measures Methods Results Conclusion
Results: Total Population
Hurdle model (part 1): Modelling the probability of havingany 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* -0.078decile 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 -8.9Net worth (ref= deciles 1-5)deciles 6-9 -36.3 -13.3decile 10 -45.5 -15.4
Background Goal Data & Measures Methods Results Conclusion
Results: Total Population
Hurdle model (part 2): Modelling the number of limitationsgiven 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 -21.4Net worth (ref= deciles 1-5)deciles 6-9 -24.2 -10.9decile 10 -25.9 -9.2
Predicted OVERALL MEAN number of mobility limitations
Total Population (top 5 occupations)
Artisans & Workers in Prod of TextilesLeather Products & related goods
Agricultural Workers
No−occupation
Workers in the Making of FoodsBeverages & Tobacco Prod
Domestic Workers
0 1 2 3
Predicted OVERALL MEAN number of mobility limitations
Males (top 5 occupations)
Artisans & Workers in ProdRepair & Maintenance
Drivers & Assistant Drivers ofMotorized Surface Transport
Agriculture, LivestockForestry & Fishing
Workers in the Making of FoodsBeverages & Tobacco Prod
Agricultural Workers
0 1 2 3
Predicted OVERALL MEAN number of mobility limitations
Females (top 5 occupations)
Workers in Service Industry
Assistants, Laborers, etc. in IndustrialProduction, Repair & Maintenance
Domestic Workers
Agricultural Workers
Workers in the Making of FoodsBeverages & Tobacco Prod
0 1 2 3
Background Goal Data & Measures Methods Results Conclusion
Conclusion:
Occupational differences account for asizeable portion of the social gradient inmobility limitations
� 20% reduction in the magnitude of the coefficient inthe educational gradient (total pop)� 10% reduction in the magnitude of the coefficient inthe net worth gradient (total pop)
Background Goal Data & Measures Methods Results Conclusion
Conclusion:
Occupational differences account for asizeable portion of the social gradient inmobility limitations
� 20% reduction in the magnitude of the coefficient inthe educational gradient (total pop)� 10% reduction in the magnitude of the coefficient inthe net worth gradient (total pop)
Background Goal Data & Measures Methods Results Conclusion
Conclusion:
Physically demanding occupations areassociated with the highest rates ofmobility limitations at older ages
The following occupations fare the worstMen: Agricultural workers, and merchants/salesrepresentativesWomen: Workers in the Making of Foods, Beverages &Tobacco Products, and agricultural workers
Background Goal Data & Measures Methods Results Conclusion
Conclusion:
Physically demanding occupations areassociated with the highest rates ofmobility limitations at older agesThe following occupations fare the worst
Men: Agricultural workers, and merchants/salesrepresentativesWomen: Workers in the Making of Foods, Beverages &Tobacco Products, and agricultural workers
