Broad societal determinants of CVD and health

Dubai 6/1/2006

Issues for the Pure study

• Health transition (Britain as an example)• Gender differences during health transition • At what age does the influence of broad social

factors on adult disease start?• (Income inequality, social capital, social

comparison)• Is a prospective study necessary?• Contingent causes, proximal causes and

understanding mechanisms

Preston. Population Studies, 1975

Economic development and health: selection of the Pure study countries may determine the answers

Health transition, coronary heart disease and stroke in Britain

Different trajectories in men and women over time, and socio-economic patterning changing

CVD trends (from Lawlor et al BMJ 2001)

0

1000

2000

3000

4000

5000

6000

1921 1931 1941 1951 1961 1971 1981 1991 1998

CHD - M CHD - F Stroke - M Stroke - F

Age-adjusted mortality rates from heart disease by sex, US 1900-98

0

100

200

300

400

500

600

700

800

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990

rate

per

100

,000 male

female

Coronary Heart Disease and stroke share risk factors

• Raised blood pressure• High blood cholesterol• Smoking• Insulin resistance / diabetes• Physical inactivity

The paradox

• So – if coronary heart disease and stroke share risk factors –

• Why are the secular trends different?– And why no epidemic of stroke during the 20th

century?

What caused the epidemic of coronary heart disease?

• Increased intake of dietary fat • Increase in smoking • Increase in obesity?• Decrease in physical activity?

BUT

• Secular decline in blood pressure

Stroke: two major pathologiesHaemorrhage Ischaemia

which are rather different…

haemorrhage Ischaemia

but look the same to doctors…

Isch

aem

ia:h

aem

orrh

age

ratio

Year

Ischaemia:haemorrhage ratio, autopsy data

Fitted values

1920 1940 1960 1980 2000

0.25

0.5

1.0

2.0

4.0

Lawlor et al. Lancet 2002

Ratio of ischaemia to haemorrhage,1930 to 2000

Mor

talit

y / 1

00 0

00

Year

Total stroke rate Estimated haemorrhagic rate

Estimated ischaemic rate

1940 1960 1980 2000

10

20

40

80

160

Stroke trends by sub-type

Lawlor et al. Lancet 2002

Risk factors by stroke subtype

Haemorrhagic

Blood pressure +++

Smoking +/-

Cholesterol –

Obesity +

Ischaemic

Blood pressure ++

Smoking +++

Cholesterol +++

Obesity ++

Changing social distribution of CVD risk factors

Moteiro CA. Bull WHO 2004;82:940-6

Age of influence of socio-environmental factors for blood

pressure

Blood pressure trends

• Marked declines among 18 year olds from 1948-1968 in UK

• Clear cohort effects in declines in blood pressure in U.S.

Early life influences on adult blood pressure

• Adverse social circumstances

• Low birthweight

• Poor height gain in early childhood (especially in leg length)

• Artificial feeding

• Infancy sodium intake

• ? Low physical activity / obesity ?

Lawlor and Davey Smith. Current Opinion in Nephrology and Hypertension 2005;14:259-64

Childhood social class and stroke subtype:

Manual vsManual vs+

non-manual non-manual

Haemorrhagic 2.84 (1.12-7.20) 3.22 (1.15-9.03)

Ischaemic 1.25 (0.77-2.03) 0.92 (0.53-1.61)+risk factor adjustedHart and Davey Smith; J Epidemiol Community Health 2003

Height quintile RR associated with 10 cm increase in height

1 2 3 4 5

All stroke

Relative rate 1 0.96 0.82 0.77 0.70 0.80 (0.73 to 0.88)

Haemorrhagic

Relative rate 1 1.16 0.93 0.66 0.54 0.70 (0.51 to 0.97)

Ischaemic

Relative rate 1 1.16 1.17 1.02 0.91 0.87 (0.71 to 1.06)

Stroke by height: Paisley and Renfrew study

McCarron et al. J Epidemiol Community Health 2001;55:404-405

Stroke subtype by number of siblings

0-2 3-4 5-6 7+ per sib per

sib+

Haemorrhagic 1 1.63 1.79 2.33 1.111.11

Ischaemic 1 0.86 1.00 1.36 1.03 1.01

+adjusted SEP and risk factorsHart and Davey Smith, J Epidemiol Community Health 2003

Systolic blood pressure mmHg (mean, 95% CI)

Systolic blood pressurea

Diastolic blood pressure

Diastolic blood pressurea

Dehydration admission

100.4 101.4 62.4 62.7

No dehydration admission

98.9 98.9 56.5 56.5

Difference 1.5 (-5.8, 8.9) 2.5 (-4.3, 9.3) 5.9 (0.6, 11.3) 6.2 (1.04, 11.4)

P difference 0.7 0.47 0.03 0.02

a adjusted for sex, age, height and BMI at the time of blood pressure measurement, birthweight and gestation, maternal education

Davey Smith et al JECH, in press

Income inequality, social capital, social comparison

Comparing Canada and the U.S.

Ross, et al., BMJ (2000)

0.15 0.19 0.23 0.27Median Share of Income

200

300

400

500

600

Rat

e pe

r 10

0,00

0 P

opul

atio

n

Working Age (25-64) Mortality by Median ShareU.S. and Canadian Metropolitan Areas

U.S. cities (n=282) with weighted linear fit (from Lynch et al. 1998)Canadian cities with weighted linear fit (n=53) (slope not significant)

Mortality Rates Standardized to the Canadian Popluation in 1991

FlorenceSC

JacksonvilleNC

SiouxCityIA

MonroeLA

ChicagoIL

TuscaloosaAL

Prince George

WashingtonDC

Oshawa

NewYorkNY

LosAngelesCA

NewOrleansLA

BryanTX

McallenTX

SiouxCityIA

AppletonWI

PortsmouthNH

PineBluffAR

FlorenceSC

MonroeLA

AugustaGA

Montreal

Vancouver

Toronto

Barrie

Oshawa

Shawinigan

GS3 Aug. 6, 1999 2:40:20 PM

Canada Paradox?

0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28

Median Share of Income

200

300

400

500

600

Wo

rkin

g A

ge

(2

5-6

4)

Mo

rta

lity

US AUSSWEUKCAN

New York

Stockholm

Sydney

Income Inequality and Working-Age Mortality528 Metropolitan Areas in Five Countries, 1990/91

Toronto

London

Ross, et al (unpublished)

American “exceptionalism”?

Income inequality in 1949 versus all-cause age-adjusted (2000 standard) mortality in 1949-51

WVWA

VA

VT

UT

TX

TN SD

SC

RI

PA

OR

OK

OH

ND

NC

NY

NM

NJ

NH

NV

NE

MT

MO

MS

MN

MI

MA

MD

ME

LA

KY

KSIA

INIL

ID

HI

GA

FL

DE

CT

CO CA

AR

AZ

AK

ALR2 = 0.011

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

30 32 34 36 38 40 42 44 46 48 50 52

Gini coefficient

Ag

e-ad

just

ed m

ort

alit

y p

er 1

00,0

00

Income inequality in 1959 versus all-cause age-adjusted (2000 standard) mortality in 1959-61

WYWI

WV

WA

VAVT

UT TX

TN

SD

SC

RI

PA

OR

OK

OH

ND

NC

NY

NM

NJ

NH

NV

NE

MT

MO

MS

MN

MIMA

MD

MELA

KY

KSIA

IN

IL

ID

HI

GA

FL

DE

CT

COCA

AR

AZ

AKAL

R2 = 0.0034

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

30 32 34 36 38 40 42 44 46 48 50 52

Gini coefficient

Ag

e-ad

just

ed m

ort

alit

y p

er 1

00,0

00

1950 1960

Income inequality in 1969 versus all-cause age-adjusted (2000 standard) mortality in 1969

WY

WI

WV

WA

VA

VT

UT

TX

TN

SD

SC

RI

PA

OR

OK

OH

ND

NC

NY

NM

NJNH

NV

NE

MT

MO

MS

MN

MI

MA

MDME

LA

KY

KSIA

INIL

ID

HI

GA

FL

DE

CTCO

CA

AR

AZ

AK

AL

R2 = 0.0466

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

30 32 34 36 38 40 42 44 46 48 50 52

Gini coefficient

Ag

e-ad

just

ed m

ort

alit

y p

er 1

00,0

00

1970

Income inequality in 1979 versus all-cause age-adjusted (2000 standard) mortality in 1979

WY

WI

WV

WA

VA

VT

UT

TXTN

SD

SC

RI

PA

OR

OK

OH

ND

NC NY

NM

NJ

NH

NV

NE

MTMO

MS

MN

MI

MA

MD

ME

LA

KY

KSIA

INIL

ID

HI

GA

FL

DE

CTCO

CA

AR

AZ

AK

AL

R2 = 0.1496

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

30 32 34 36 38 40 42 44 46 48 50 52

Gini coefficient

Ag

e-ad

just

ed m

ort

alit

y p

er 1

00,0

00

1980

Income inequality in 1999 versus all-cause age-adjusted (2000 standard) mortality in 1999

WY

WI

WV

WA

VA

VT

UT

TX

TN

SD

SC

RI

PA

OR

OK

OH

ND

NC

NY

NMNJ

NH

NV

NEMT

MO

MS

MN

MI

MA

MDME

LA

KY

KS

IA

IN

IL

ID

HI

GA

FL

DE

CTCO

CA

AR

AZAK

ALR2 = 0.1917

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

30 32 34 36 38 40 42 44 46 48 50 52

Gini coefficient

Age

-adj

uste

d m

orta

lity

per

100,

000

Income inequality in 1989 versus all-cause age-adjusted (2000 standard) mortality in 1989

WY

WI

WV

WA

VA

VT

UT

TX

TN

SD

SC

RI

PA

OR

OKOH

ND

NC NY

NM

NJ

NH

NV

NE MT

MO

MS

MN

MI

MA

MD

ME

LA

KY

KSIA

INIL

ID

HI

GA

FL

DE

CT

CO

CA

AR

AZ

AK

AL

R2 = 0.3357

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

30 32 34 36 38 40 42 44 46 48 50 52

Gini coefficient

Age

-adj

uste

d m

orta

lity

per

100,

000

1990

2000

Material conditions or social comparison?

Check-In Seat Leg

Room Food Service

Sleeping

Health Relevant Material Conditions?

What generates health inequalities after a

long airline flight?

Or: seeing Martin McKee in business class?

Measures of social position can give some insight ..

Unadjusted Odds Ratios and percent reduction of elevated risk on adjustment for

mortality by caste and standard of living indexInfant

(<1 year)

Caste Standard of living index

Other Caste 1.00 - Top fifth 1.00 -

SC 1.36 64% Fourth fifth 1.67 13%

ST 1.44 68% Third fifth 2.13 15%

OBC 1.27 56% Second fifth 2.64 16%

No Caste 1.39 44% Bottom fifth 3.09 15%

Subramanian et al, AJPH, in press

Unadjusted Odds Ratios and percent reduction of elevated risk on adjustment for

mortality by caste and standard of living index

Young adult

(19-44 years)

Caste Standard of living index

Other Caste 1.00 - Top fifth 1.00 -

SC 1.29 97% Fourth fifth 1.66 -2%

ST 1.87 47% Third fifth 1.91 0%

OBC 1.16 94% Second fifth 2.11 2%

No Caste 1.02 200% Bottom fifth 3.00 4%

Unadjusted Odds Ratios and percent reduction of elevated risk on adjustment for

mortality by caste and standard of living index

Middle-aged adult

(45-64 years)

Caste Standard of living index

Other Caste 1.00 - Top fifth 1.00 -

SC 1.23 87% Fourth fifth 1.19 -16%

ST 1.28 89% Third fifth 1.40 -10%

OBC 1.08 113% Second fifth 1.65 -6%

No Caste 1.14 43% Bottom fifth 1.91 -7%

Is a prospective study necessary?

Bias

Illustrations of how bias can generate associations between self-reported

exposures and outcomes

Relative rates for CHD outcomes according to stress

Incident Incident CHDStress angina ischaemia mortality

High 2.63 0.58 0.96

Medium 1.36 0.88 0.95

Low 1.00 1.00 1.00

p for trend <0.001 0.10 0.69Macleod et al BMJ 2001

Reporting tendency, angina and psychological stress

Perceived stress Mean reporting tendency score

Incident angina

Odds ratios

Adjustment A Adjustment B

High 0.77 2.63 2.28

Medium 0.52 1.36 1.27

Low 0.41 1.00 1.00

p for trend <0.001 <0.001 0.003

Macleod J et al. JECH 2002;56:76-77

Job control

Odds ratio for incident “Rose” angina

Low control 2.02 (1.22-3.34); Intermediate control 1.44 (0.86-2.39);High control 1.00

Bosma et al. BMJ 1997;314:558-65.

Fully subjective

High Exposure 2.66 Medium Exposure 1.37 Low Exposure 1.00

High Exposure 2.02 Medium Exposure 1.44 Low Exposure 1.00

Partially subjective

High Exposure 1.20 Medium Exposure 1.03 Low Exposure 1.00

High Exposure 1.65 Medium Exposure 1.13 Low Exposure 1.00

Fully objective

High Exposure 0.67 Medium Exposure 1.03 Low Exposure 1.00

High Exposure 1.17 Medium Exposure 1.16 Low Exposure 1.00

Macleod J et al. BMJ 2002;324:1247-51.Bosma H et al. BMJ 1997;314:558-65.Stansfeld SA et al. Int J Epidemiology 2002;31:248-255.

Contingent causes, proximal causes and understanding

mechanisms

TIME

CB

OUTCOMEMEASURE

Contingent links

Context dependentunderlying factors

TIMELUNGCANCER

Contingent links

Social position Smoking

TIMEOUTCOMEMEASURE

Contingent links

Context dependentunderlying factors BMI Insulin

resistance