Surprising SES gradients in mortality,health, and biomarkers in a Latin

American population of adultsLuis Rosero-BixbyUniversity of Costa Rica

William H. DowUniversity of California at Berkeley

Support from the Wellcome Trust

Rosero-Bixby, 1993

life expectancy vs. gross domestic product

Infant Mortality

Year

1960 1970 1973 1980 1990

0

20

40

80

60

Infant Mortality Trend, 1960-1990

Some Possible Explanations for Good Health

• Health care: Good access? Equitable access? High quality? Primary health focus? Insurance?

• Public health: Clean water? Sanitation, air quality?

• Health behaviors: Good diet? Smoking, exercise, obesity? Modern health beliefs?

• Historical Accident: Temperate climate? Genes?

• Social determinants: High female education? Low poverty? Social equity and inclusion? Low stress society?

Costa Rica: middle income country, high social development, strong public sector,

advanced demographic transition

Source: World Bank

Indicator CR Mexico USA

Per capita income (US$2007) 5,560 8,340 46,040

Life expectancy at birth (years, 2006) 79 74 78

Total fertility rate (births) 2.00 2.20 2.04

Population ages 65+ (%) 5.6 5.1 12.3

Seniors with health insurance (% of population 65+)

96 62 100

Public health expenditure (% of health expenditures)

78.8 46.4 44.6

SES differentials shed light on good health in CR?

• Does public health lead to less exposure among low SES, despite few resources?

• Does health care access buffer effects of exposures from low SES?

• Is there a smaller gradient, consistent with stress stories?

• First step: document differentials

Small SES gradients in CR health?

• Research elsewhere finds SES gradients persist:– Into old age (though shrink with age)– Even with good health care access

• But previous work shows little CR adult mortality gradients.– Is this true of other health indicators?

Previous work:

Insurance and other determinants of elderly longevity in a Costa Rican panel

Rosero-Bixby, Dow, and Lacle

Journal of Biosocial Science2005

Mortality data

• Panel of 876 individuals aged 60+ in 1984

• Semi urban community near San Jose (100% sample from the 1984 census)

• Observed from June 1984 to December 2001

• Interview data from the 1984 census and visits in 1985 and 1986

• Survival from 1988 and 2002 contacts, and computer follow up in the civil registration.

Result 3. No clear SES effect

0 5 10 15analysis time

Poor

Middle

Rich

B. Wealth index in 1984

New data from CRELES: Costa Rican study of Longevity and

Healthy Aging

• National sample of 8,000 born before 1946, from the 2000 census.– 6-year survival follow up

• Sub-sample of 3,000 interviewed in 2005-6:– First wave of a panel (resurvey 2007,2009)– 90 minute interview and 10 minute diet– Anthropometry, fasting blood and overnight

urine samples

Study framework: 3 levels of health indicators

Level 3Level 1 Level 2

All indicators are poor-health dummiesControl demography with logistic regression

Demography Age Sex Marriage

SES Education Income & wealth Place development

Health behavior& Lifestyles Smoking Exercising Diet Social support Depression Seeking care Health care supply

Biological risks Blood pressure Cholesterol Triglycerides Obesity Creatinine Cortisol Epinephrine APoe gene Etc...

Health outcomes Mortality Metabolic syndrome Functional decline Physical decline Cognitive impairment Self reported health

Table 1. SES and demographic variable in the mortality sample and prevalence sub-sample SES and demographic Mortality sample Prevalence subsample variables N % N % Total (8,145) 100 (2,825) 100 Residence

Metro SJ (2,352) 29 (686) 24 High lands (3,140) 39 (1,043) 37 Low lands (2,653) 33 (1,096) 39

Education None (1,653) 20 (529) 19 Elementary (5,286) 65 (1,902) 67 Secondary + (1,206) 15 (394) 14

Wealth Poor (1,989) 24 (674) 24 Middle (5,249) 64 (1,879) 66 Rich (907) 11 (272) 10

Demographic controls Age

60-69 (2,107) 27 (845) 30 70-79 (2,151) 27 (940) 33 80-89 (1,800) 23 (764) 27 90+ (1,803) 23 (278) 10

Sex Female (4,289) 53 (1,532) 54 Male (3,856) 47 (1,293) 46

Marital status Non married (4,058) 50 (1,414) 50 Married (4,087) 50 (1,403) 50

Notes: Age at the middle of the mortality period (2000/6) or at the interview in 2005/6 Marital status at the interview for the prevalence subsample

Health Outcomes by Age

Ages Ratio General health outcomes

60 -79 80 + 80/60

Death rate (per 1,000) 21.4 128.9 6.02 Poor SRH 46.7 50.4 1.08 Functional disability (50%+ of 14 ADL/IADL) 7.3 37.4 5.11 Physical frailty (2+ out of 5 tests) 17.8 57.0 3.21 Cognitive impairment (<75% of 15 Minimental) 6.6 36.9 5.55 Metabolic syndrome 48.5 38.8 0.80

Poor-health biomarkers by age

Ages Ratio Biomarkers

60 -79 80 + 80/60 Diabetes (Glyco hemog.>= 6.5% or medicine) 24.5 16.9 0.69 High blood pressure (>=140/90 mmHg or medicine) 59.0 63.4 1.07 High triglycerides (>=150 mg/dl) 45.8 39.3 0.86 High cholesterol ratio (Total/HDL >=5.92) 29.3 23.6 0.81 Low creatinine clearance (<=44.64 mg/dl) 12.9 33.1 2.57 High cortisol (>=25.69 ug/g) 33.1 49.3 1.49 Low DHEA-S (<=35 mg/dl) 38.6 59.2 1.53 High epinephrine (>=4.99 ug/g) 46.1 52.2 1.13 High norepinefrine (>=48 ug/g) 25.2 32.5 1.29 High CRP (>=10.0) 10.9 14.9 1.36 Geriatric depression (9+ out of 15 Yesavage items) 9.3 10.0 1.07 Weak grip strength (<20/30 kg M/F) 27.6 74.3 2.69

Poor-health lifestylesAges Ratio

Unhealthy life styles 60 -79 80 + 80/60

Obese (IMC > 30) 28.3 13.7 0.48 Abdominal girth (>= 94/80 cm M/F) 69.3 62.2 0.90 Smoking current 10.8 5.4 0.50 No regular exercise in last year 65.5 86.4 1.32 No flu vaccine last year 54.0 32.6 0.60 High calorie diet (>3000 day) 13.1 7.7 0.59 Low calorie diet (<1500 day) 15.3 21.6 1.42 High carbohydrate diet (>400g day) 16.4 10.2 0.62 High fat diet (>40g day) 14.2 12.3 0.87

The low old-age mortality in Costa Rica challenges the notion of an SES gradient

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CR 95% CI

Japan and USA rates adjusted to the sex composition of Costa Rica

The puzzling SES gradient:mortality vs. self-reported health(controlling for age, sex, marital)

.5

.75

1

1.25

1 2 31=Lowlands, 3= Metro SJ

By residence place

1 2 31=No education, 3=High school+

Mortality Poor-SRH

By education

.5

.75

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1.25

1 2 31=Poor, 3=Rich

By wealth level

The puzzling SES gradient 2

.5

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1 2 31=No education, 3=Post elementary

By education

.5

.75

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1 2 31=Poor, 3=Rich

Function decline Fitness dec.

Cognitive dec. Metab. Synd.

By wealth level

Health-SES gradients from logistic OR(Controlling age, sex, marital. *p<.05)

Explanatory variables

Death Poor SRH

Functional disability

Physical frailty

Cognitive disability

Metabolic syndrome

Residence Low lands 0.90 * 1.50 * 0.75 * 0.51 * 0.76 * 0.88 * High lands 1 1 1 1 1 1 Metro San Jose 0.92 * 0.85 0.82 0.87 0.71 * 1.20

Education None 0.92 1.19 1.46 * 1.17 2.91 * 0.80 Elementary 1 1 1 1 1 1 High school+ 1.13 0.42 * 0.60 * 0.62 * 0.43 * 0.92

Wealth status Low wealth 0.99 1.39 * 1.19 1.34 * 1.36 * 0.80 Medium 1 1 1 1 1 1 High wealth 1.08 0.69 * 0.72 0.59 * 0.88 0.92

Biomarker-SES gradients from logistic OR(Controlling age, sex, marital. *p<.05)

Poor health indicators Low lands Metro SJ

No school

High school

Low wealth

High wealth

Joint SES Prob > chi2

Poor-health biomarkers Diabetes 0.85 1.02 1.01 0.91 0.81 0.86 0.31 High blood pressure 0.80 * 1.14 0.90 0.89 0.84 0.84 0.01 * High triglycerides 0.91 1.14 0.78 * 1.01 0.80 * 0.76 0.00 * High cholesterol ratio 0.81 1.18 0.68 * 0.83 1.10 0.90 0.08 Low creatinine clearance 0.97 1.29 1.42 * 1.30 1.05 1.43 0.03 * High cortisol 0.83 0.86 1.21 1.01 1.04 0.85 0.49 Low DHEA-S 0.65 * 0.74 * 0.93 1.10 0.83 1.16 0.00 * Highepinephrine 0.58 * 0.50 * 0.79 0.84 1.03 0.83 0.04 * High norepinefrine 0.45 * 0.26 * 1.23 0.91 1.10 1.20 0.00 * High CRP 1.02 1.16 0.94 0.73 1.10 0.92 0.54 Geriatric depression 1.58 * 1.16 1.05 0.77 1.50 0.74 0.00 * Weak grip strength 0.77 * 1.01 1.11 0.70 * 1.41 * 0.76 0.00 *

Lifestyle-SES gradients from logistic OR(Controlling age, sex, marital. *p<.05)

Unhealthy life styles Low lands Metro SJ

No school

High school

Low wealth

High wealth

Joint SES Prob > chi2

Obese (IMC > 30) 1.10 1.28 * 0.85 0.85 0.70 * 1.08 0.00 * Abdominal girth 0.86 1.09 0.78 * 0.85 0.72 * 1.36 0.00 * Smoking current 0.79 1.15 1.33 0.95 1.85 * 0.59 0.00 * No regular exercise 1.13 1.38 1.24 0.57 * 0.85 0.75 * 0.00 * No flu vaccine last year 1.08 1.53 * 0.82 1.01 1.15 1.13 0.11 High calorie diet 1.30 1.33 * 0.88 1.30 0.69 1.20 0.01 * High calorie diet 0.83 0.86 1.69 * 0.87 1.29 0.95 0.00 * High carbohydrate diet 1.22 1.09 1.08 0.64 1.07 0.72 0.21 High fat diet 1.03 0.99 0.90 1.28 0.75 0.95 0.56

Summary• Flat mortality gradient contrasts other

measures.• Quality of life shows strong gradient.• CVD is major cause of death, so lack of

mortality gradients reflects mixed CVD risk factors:– Smoking, low exercise worse for low SES– Diabetes and hypertension not related to SES– Cholesterol and obesity worse for high SES

(worse diets)=> Lack of mortality gradient not imply Costa

Rica has eliminated SES-health gradient

Reflects nutritional transition?

• Possible that Costa Rica is early in nutritional transition, and SES gradients in nutrition-related indicators will flip.

• But external surveys show female obesity (BMI>25) rising for decades:– 1982: 56% women age 45-59 overweight– 1996: 75% women age 45-59 overweight

What Next?• New data:

– 1984 census-mortality linkage to measure SES trends over time.

– Younger cohort: 1945-55 birth cohorts.

• Further analyses:– Compare gradients to other countries.

• Rehkopf: comparison with U.S. NHANES

– Test if stress has small relation to health.• Gersten: life stressor and neuroendocrine allostatic load• Modrek: inequality and health

– Investigate role of health care: hypertension.

Differences in the association of cardiovascular risk factors with education: a comparison of Costa Rica (CRELES) and the United States (NHANES)

David H. Rehkopf, University of California, San Francisco, Department of Epidemiology and Biostatistics

William H. Dow, University of California, Berkeley, Department of Health Policy and Management

Luis Rosero-Bixby, Universidad de Costa Rica, Centro Centroamericano de Poblacion

Objectives of this paper

• Compare risk factor levels across countries

• Compare education gradients in risk

• Inexact education comparison – so focus on direction of gradients

data

• Costa Rica (Costa Rican Healthy Aging Study)• 2000-2006, n = 2827, age 60+, 17 outcomes• education: 0-2, 3-6, 7+

• United States (National Health and Nutrition Examination Survey)

• 1999-2004, n= 5607, age 60+, 17 outcomes• education: <12, 12, 12+

17 outcomes• behavioral• current smoking, lifetime smoking, sedentary, high

saturated fat, high carbohydrates, high calorie diet

• Anthropometric• obese, severely obese, large waist, body mass index

• biomarkers• HDL cholesterol, LDL cholesterol, triglycerides,

hemoglobin A1c, fasting glucose, C-reactive protein, systolic blood pressure

Table 1/Figure 1: Comparing means• Age and marital distributions roughly similar. Education not easily

comparable.• Smoking:

– Men: Similar.– Women: Lower in CR.

• Diet: Comparability concerns, but CR appears lower fat, maybe worse other dimensions.

• Obesity: – Men: CR much lower– Women: CR only slightly lower.For men, CR much lower; for women CR

only slightly lower than US.

• Hypertension, cholesterol, diabetes:– Men: CR lower than US (diabetes ~same)– Women: CR similar (diabetes higher than US)

comparison of means of biological risk factors for cardiovascular risk factors

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H. Body Mass Index (kg/m2)

Den

sity

• Current smoking

• Lifetime smoking

• Sedentary

• Saturated fat

• carbohydrates

• High calorie diet

• obese

• Severely obese

• Large waist

Costa Rica United States

men women men women

CRELES - MEN

curr

ent s

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1 CRELES - WOMEN NHANES - MEN NHANES - WOMEN

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• HDL cholesterol

• LDL cholesterol

• Triglycerides

• Hemoglobin A1c

• Fasting glucose

• C-reactive protein

• Systolic blood pressure

• Body mass index

Costa Rica United States

men women men women

Figures 2/3: Education gradients (from regressions controlling for age)

• Smoking: – Males: gradient both US and CR– Females: gradient only in CR

• Diet: High calorie– Reverse gradient especially in CR

• Obesity: – Males in CR have reverse gradient.– Females have expected gradient (both US, CR).

• Cholesterol: – HDL: only US women have expected gradient– LDL: CR men have gradient

• Blood pressure: gradient only in CR men• HbA1c: Expected gradients, except none in CR men• C-reactive protein: Expected gradient in US, but none in CR

Summary• Mixed gradients tell complex story, raise more

questions.• C-reactive protein: why no CR gradient?

(obesity, or buffers?)• Obesity worrisome in CR:

– Women already close to US levels.– Male reverse gradient: low SES may rise next.

• Next steps:– Study time trends in mortality by SES and cause of

death (1984 census-mortality linkage)– Examine treatment for hypertension, cholesterol,

diabetes. Why are levels so high when medicines can help control? Why are there gradients in (male) blood pressure and LDL control in CR’s vaunted system?

Education differentials in coronary heart disease mortality among

those 60 and olderCosta Rica United States

CRELES – all ages, all-cause

Life stressors and neuroendocrine allostatic load in Costa Rica

Omer Gersten, Ph.D.Academia Sinica

Population Association of AmericaDetroit, Michigan

May 1, 2009

Research question

• Is greater AL predictive of worse health outcomes?

• Are various indicators of life stress linked to greater AL?

Year 2004-6

Earlier life history/ ------------------------> BiomarkerCurrent situation

collection/survey

Research hypothesisEarly life events |

low edu. of mother |live w/out biological father |econ. problems (index) |health problems (index) |

Loss |death of children | widowhood/years widowed | 2004-2006

Social deprivation | ---------------------> High AL low/no church attendance |lives alone |

Spousal characteristics |low edu. |poor health |

Demographic |low edu. |rural residence |

Economic |low household wealth |

Data: dependent variable“Neuroendocrine allostatic load” (NAL)

Biomarkers Physiologic sub-systems Physiologic system

Epinephrine ----------> Sympathetic nervous |Norepinephrine system (SNS) |

|----> NeuroendocrineCortisol ----------------> Hypothalamic-pituitary- | DHEAS adrenal (HPA) axis |

• Epi., norepi., & cortisol initiate body’s most immediate stress response

• Survey measures resting, nonstressed levels

Dependent variable: NAL Model 1 Model 2 Model 3 Model 4 Independent variables Demographic Age 0.02 (0.000) 0.02 (0.000) 0.02 (0.000) 0.02 (0.000) Female sex 0.41 (0.000) 0.37 (0.000) 0.40 (0.000) 0.37 (0.000) Low education (< 6 years) -0.09 (0.116) -- -- -- Rural residence (v. urban) -0.01 (0.841) -- -- -- Immigrant (v. native born) -0.22 (0.043) -- -- -- Economic resources Household wealth 0.03 (0.588) -- -- -- Monthly income (in thousands) -0.00 (0.215) -- -- -- Self-assessed economic situation 0.02 (0.504) -- -- -- Social deprivation Currently unmarried (v. curr. married) -- 0.09 (0.217) -- -- Lives alone -- 0.02 (0.872) -- -- Low church attendance (< weekly) -- 0.03 (0.603) -- -- Loss No. of children who have died (>= 1) -- -- 0.10 (0.184) -- Early childhood conditions Maternal age at death (years) -- -- -- 0.00 (0.432) Low maternal education (no education) -- -- -- -0.05 (0.517) Lived without biological father -- -- -- -0.06 (0.535) Poor health (>= 1 health problems) -- -- -- -0.12 (0.093) Economic deprivation index -- -- -- 0.01 (0.713)

Conclusions

Q: Are early and other negative life events linked to riskier neuroendocrine allostatic load (NAL) levels?

A: No.

Gracias!

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http://ccp.ucr.ac.cr