Int. J. Epidemiol. Advance Access published April 14, 2005
Published by Oxford University Press on behalf of the International Epidemiological Association
© The Author 2005; all rights reserved.
International Journal of Epidemiology
doi:10.1093/ije/dyi063
Self-reported economic difficulties and
coronary events in men: evidence from the
Whitehall II study
JE Ferrie,1* P Martikainen,2 MJ Shipley1 and MG Marmot1
Accepted
1 March 2003
Methods
The data come from 5021 middle-aged, white-collar men in the Whitehall II
study. Self-reported household financial problems, measured at baseline
(1985–88) and Phase 3 (1991–93), were used to construct a five-category score of
persistent economic difficulties. Associations between economic difficulties and
incident coronary events were determined over an average follow-up of 7 years.
Other socioeconomic, psychosocial, behavioural and biological explanatory
variables were obtained from the Phase 3 questionnaire and clinical examination.
Results
Age-adjusted Cox regression analyses demonstrated steep gradients in the incidence
of coronary events with economic difficulties. The relative hazard between the
bottom and the top of the difficulties hierarchy was 2.5 (95% confidence intervals
(CI) 1.2–5.2) for fatal and non-fatal myocardial infarction (MI), 2.1 (1.3–3.6) for MI
plus definite angina and 2.8 (1.9–4.2) for total coronary events. Adjustment for
other markers of socioeconomic position, early life factors, psychosocial work
environment characteristics and health-related behaviours had little effect, while
adjustment for the biological factors reduced the association between difficulties
and coronary events by 16–24%.
Conclusion
We have demonstrated an economic difficulties gradient in coronary events in
men that is independent of other markers of socioeconomic position and appears
to be only partially mediated by well-known risk factors in mid-life.
Keywords
Behavioural, biological, CHD, coronary events, economic difficulties, household,
early life, social gradient, work environment characteristics
Numerous studies have demonstrated social inequalities in
coronary heart disease (CHD) using a variety of measures of
social position, the most common being occupation, education
1 International Centre for Health and Society, Department of Epidemiology
and Public Health, UCL Medical School, London, UK.
2 Population Research Unit, Department of Sociology, University of Helsinki,
Helsinki, Finland.
* Corresponding author. International Centre for Health and Society,
Department of Epidemiology and Public Health, University College London
Medical School, 1-19 Torrington Place, London WC1E 6BT, UK.
E-mail: [email protected]
and income.1–4 While social inequalities are usually found
regardless of the measure used, the strength of the association
differs between measures (P. Martikainen unpublished work).
For example, social position measured using the Cambridge
scale, in which social classes are defined by similarities in lifestyle and resources, has been shown to have a stronger linear
association with CHD than the Registrar General’s Social
Classification, which is based on occupational standing.5 These
different measures of social position tap into different dimensions of inequality and consequently adjustments of social
gradients in CHD for potential confounders and mediating
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Background Numerous studies have demonstrated social inequalities in coronary heart
disease using a variety of measures of social position. In this study we examine
associations between persistent economic difficulties and serious coronary
events. Our aim is to assess whether these associations are (i) explained by other
measures of socioeconomic status, and (ii) mediated by psychosocial, behavioural
and biological factors.
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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Data and methods
Participants
The target population for the Whitehall II study was all Londonbased office staff, aged 35–55, in 20 Civil Service departments.
With a response rate of 73%, the final cohort consisted of
10 308 participants: 6895 men and 3413 women.13 The true
response rate would have been higher but ~4% of those invited
were ineligible. Although mostly white-collar, respondents
covered a wide range of employment grades from messenger
to permanent secretary, the highest grade in the British
Civil Service.
Measures
Economic difficulties
A measure of economic difficulties was constructed from two
questions from Pearlin’s list of chronic strains,14 available from
both the Phase 1 and Phase 3 questionnaires. These questions
asked about difficulty in the payment of bills and how often the
participant lacked sufficient money to afford the kind of food
or clothing he/she or the family should have. The response
categories were scored as follows: ‘never’ or ‘very little’ = 0;
‘seldom’ or ‘slight’ = 1; ‘sometimes’ or ‘some’ = 2; ‘often’ or
‘great’ = 3; and ‘always’, ‘very great’ or ‘very great
problems’ = 4, giving a maximum score for economic difficulties
of 8 at each phase. As these questions were introduced after
the start of the baseline survey, values from the Phase 2 survey
(1989–1990) were used where baseline data were missing. The
measure has a high internal consistency with correlations of
~0.6 at both phases.
A persistent economic difficulties score was constructed from
the score at Phase 1/2 and Phase 3. Participants in the reference
group had an economic difficulties score of 0 at both phases,
while participants in the highest category had a score of 3+ at
both phases, see Box 1.
Personal details
Age and employment grade were derived from the Phase 3
questionnaire. Employment grade was determined from the
participant’s Civil Service grade title. For analysis, employment
grade titles were divided into six categories in order of
decreasing salary. Grade 6 represented the lowest status jobs
and was defined as low employment grade.
Markers of early life
Father’s social class was determined at baseline using the
Registrar General’s classification. Data from Phase 6 were used
for participants with missing values. Participants whose fathers
were from classes III manual, IV and V formed the group
father’s social class manual. Height was measured to the nearest
millimetre.
Behavioural risk factors
From the Phase 3 questionnaire, three health-related
behaviours were examined: alcohol, exercise, and smoking.
High alcohol consumption was defined as 22 or more
units/week, which is the recommended limit for safe drinking
among men used in the UK General Household Survey.15 Based
on energy utilization, self-reported leisure-time physical
activity was categorized as vigorous, moderate and mild. The
Data collection
Baseline screening (Phase 1) took place between late 1985 and
early 1988. This involved a clinical examination in which height,
weight, blood pressure, and serum cholesterol were determined,
among other anthropometric and biomedical measures. A selfadministered questionnaire containing sections on demographic
characteristics, health, lifestyle factors, work characteristics, social
support, life events and chronic difficulties was completed by
each respondent. In Phase 2 (1989–90) the same questionnaire
data were collected by post. Since then data collection phases
including a questionnaire and clinical examination, in Phase 3
(1992–93) and Phase 5 (1997–99), have alternated with
questionnaire only data collection, in Phase 4 (1995–96) and
Phase 6 (2001).
Box 1 Categorization of economic difficulties scores
Economic difficulties score Description
0/0
A score of 0 both at Phase 1 and
Phase 3 (reference group)
0/1–2
A score of 0 at either Phase 1 or
Phase 3 and 1–2 at the other phase
1–2/1–2
A score of 1–2 both at Phase 1 and
Phase 3
0–2/3+
A score of 0–2 at either Phase 1 or
Phase 3 and 3+ at the other phase
3+/3+
A score of 3+ both at Phase 1 and
Phase 3
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factors have tended to produce different findings. For example,
income group differentials in the incidence of acute myocardial
infarction among men in Finland6 and all-cause mortality
in a nationally representative study in the US7 were not
explained away by behavioural risk factors, while differences
among educational groups were. It has thus been proposed
that we might move further towards understanding the
causal pathways between social circumstances and health
if we distinguish between the different dimensions of
inequality.8,9
Although income has often been used as a marker of
socioeconomic position, its use is much less common than
either education or occupation-based social class. Studies that
have examined associations between income and CHD using
individual level data are still relatively rare and few studies
have addressed the discrepancy between actual income
and disposable income or spending power. The few studies
that have examined disposable income have documented
adverse associations with health outcomes,10,11 but even these
fail to take account of demands on disposable income or
adequacy of income. In most industrialized countries the largest
drain on disposable income is housing costs including mortgage repayments, followed by bills and expenditure on food
and clothing.12
In this study we examine associations between persistent
inability to meet bills and/or buy appropriate food and clothing
and serious coronary events. Our aim is to assess whether these
associations are (i) explained by other measures of
socioeconomic status, and (ii) mediated by psychosocial,
behavioural and biological factors. The data come from the men
in the Whitehall II study.
ECONOMIC DIFFICULTIES AND CORONARY EVENTS
‘little exercise’ category comprised those who did 1 h of either
moderate or vigorous activity per week. Participants were
allocated to one of four smoking categories; never, ex-smoker,
pipe and/or cigar only, or current cigarette smoker (manufactured or hand-rolled cigarettes). Adjustment for smoking also
included the number of cigarettes smoked per day.
Work environment characteristics
Four psychosocial characteristics of the work environment were
derived from the Phase 3 questionnaire. Low control and high
demands at work were based on the Job Strain Model.19 Low
work support had three components; support from colleagues,
support from supervisors, and sufficient and consistent
information from supervisors. Responses on a four-point scale
from ‘often’ to ‘never/almost never’ were combined into
summary scales and then divided into tertiles. A high effort/
reward ratio represented a high level of imbalance between
extrinsic effort expended and reward received.20
Outcome measures
In the analyses, three measures of incident coronary events
between Phase 3 and the end of Phase 5 were examined.
Myocardial infarction (MI) was defined as a coronary death
(ICD 9 codes 410–414) or non-fatal MI verified in clinical
records. Potential cases of non-fatal MI were ascertained by
questionnaire items on chest pain,21 and doctor’s diagnosis of
heart attack. Details of physician diagnoses and investigation
results were sought from clinical records for all potential cases
of MI. Twelve lead resting electrocardiograms were performed
at Phases 3 and 5 (Siemens Mingorec) and assigned Minnesota
codes.22 Based on all available data (from questionnaires, study
electrocardiograms, hospital acute ECGs and cardiac enzymes),
non-fatal MI was defined following MONICA criteria.23
Classification of MI was carried out blind to other study data
independently by two trained coders, with adjudication by a
third in the (rare) event of disagreement. MI plus definite
angina included, in addition to fatal and non-fatal MI,
participants who reported symptoms of angina,24 with
corroboration in clinical records or abnormalities on a resting
ECG, exercise ECG, or coronary angiogram. In addition to the
above, total coronary events included self-reported cases in the
absence of any clinical record evidence of coronary disease. All
three outcomes comprised incident events only and all
participants with prevalent CHD (including angina) at Phase 3
were excluded.
Study sample and statistical analysis
Of the 6895 men who participated in the baseline screening
83% (5739) completed a full questionnaire at Phase 3.
Economic difficulties data were only available at Phase 1 for
75% (3747) of the men included in these analyses. Data for
the remaining 25% were taken from the Phase 2 questionnaire.
The 5021 men included in these analyses comprise those with
data on economic difficulties, employment grade and smoking,
and who did not have prevalent coronary events at Phase 3
(420 cases).
Phase 3 formed the baseline for the follow-up of incident
coronary events. In the 7 years from Phase 3 to the end of
follow-up there were 301 coronary events in men, including 85
cases of definite angina and 92 MIs. The paper examines
associations in men as there were only 11 MIs among the 2142
women.
Event rates were calculated using person years at risk and
standardized for age at Phase 3 by the direct method.
Associations between the economic difficulties score and other
risk factors with incident coronary events were described using
hazard ratios and 95% confidence intervals (CI), computed
using Cox’s proportional hazards models. The overall effect of
the economic difficulties score, comparing the highest vs the
lowest difficulties category, was summarized using the relative
index of inequality (RII).25 This index, for each individual, is a
score on a scale from 0 to 1 equal to the proportion of the
sample that has higher economic difficulties. It overcomes
the problem of comparing small groups at the extremes of the
difficulties score distribution, as it takes into account both the
population size and the relative position of the economic
difficulties in all five score categories. In the analysis of coronary
events, the RII shows the ratio of the instantaneous event rates
between the extremes of the economic difficulties distribution
(highest vs lowest difficulties).
Analyses to estimate the joint effects of other markers of
socioeconomic position, early life, behavioural and biological
factors and work characteristics on the RII in incident coronary
events for the economic difficulties score, resulted in
approximately a quarter of the participants having data missing
for one or more variables. In order to avoid having a selected
dataset for multivariate analyses (in Table 3), multiple imputed
values were generated for the missing data using the program
NORM,26 from which five datasets were randomly selected.
Analyses conducted on each of these five datasets gave very
similar results and it is the mean of these estimates that is
presented. The standard errors for these means are computed
as the average standard error across the five datasets plus a
term that allows for the variation in estimates across the five
imputations. Since the RII summarizes a gradient across the
whole range of economic difficulties, we have estimated effects
of adjustment for other factors on the RII by calculating
percentage changes using the logarithm of the hazard ratio for
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Biological risk factors
All the risk factors below were measured at the Phase 3
screening examination. Fibrinogen in grams/litre (g/litre) was
determined by immunoturbidimetric methods.16 Blood pressure
in millimetres of mercury (mm Hg) was measured twice with
the participant seated after a 5 minute rest, using a Hawksley
random-zero sphygmomanometer. Total, HDL cholesterol, and
triglyceride concentration in millimoles/litre (mmol/litre) were
measured using enzymic colorimetric methods, and waist and
hip circumference was measured as previously described.16 LDL
cholesterol was calculated using the Freidewald equation.17
Body Mass Index (BMI) was calculated from weight in kilograms
(kg) and height in metres (m) as kg/m2.
Oral glucose tolerance tests were administered following an
overnight fast or in the afternoon after no more than a light fatfree breakfast taken before 8.00 hours. Plasma glucose and
serum insulin were measured respectively by an electrochemical glucose oxidase method and by radioimmunoassay.16
Insulin resistance was estimated according to the homeostasis
model assessment (HOMA),18 as the product of fasting glucose
and insulin, divided by the constant 22.5. Higher HOMA scores
indicate greater insulin resistance. Diabetic included all known
diabetics.
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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
the RII. Apart from the imputations, all statistical analyses were
performed using the software package SAS 8.2.
Results
Early life factors. There is no association between economic
difficulties and father’s social class, but a strong direct
association with height.
Health related behaviours. A strong economic difficulties
gradient is observed for little exercise and current cigarette
smoking, but there appears to be no association between
economic difficulties and alcohol consumption.
Biological factors. In these data there is no evidence of an
association between economic difficulties and systolic blood
pressure or any measure of cholesterol, except HDL (P = 0.003).
However, there is a strong economic difficulties gradient in
triglyceride level, waist-hip ratio, BMI, and insulin resistance
(P 0.001) and evidence of a gradient in diastolic blood
pressure (P = 0.03) and diabetes (P = 0.02). No gradient is
observed for fibrinogen.
Work environment characteristics. Strong, economic difficulties
gradients are observed for all the psychosocial work
characteristics (P 0.005).
Table 3 shows the RII for the association between economic
difficulties and coronary events after adjustment for other risk
Discussion
Synopsis of results
We observed a steep gradient between persistent economic
difficulties in mid-life and coronary events in white-collar men.
The markers of adult socioeconomic position, low employment grade and housing tenure, were highly correlated with
economic difficulties, while car access showed a non-linear
association. However, the associations between economic
difficulties and coronary events were independent of all these
socioeconomic markers.
In analyses adjusted for age, height but not father’s social
class was associated with economic difficulties. By contrast
there were gradients in lack of exercise, current smoking,
diastolic blood pressure, HDL cholesterol, triglyceride, waist-hip
ratio, BMI, insulin resistance, diabetes and all the psychosocial
characteristics of the work environment. However, less than
one-quarter of the gradients in coronary events generated by
persistent economic difficulties appeared to be accounted for by
these psychosocial, behavioural and biological measures.
Relationship to previous work
In addition to economic difficulties, five measures of socioeconomic position were examined in this study. One of the two
early life factors, father’s social class, was not associated with
economic difficulties in adulthood. This was unexpected as
previous work has shown that father’s social class, as a marker
of advantage or disadvantage in childhood, is correlated with
Table 1 Incidence of coronary events by economic difficulties
Economic difficulties
score
No. of
subjects
Total coronary events
MI plus definite angina
MI (fatal/non-fatal MI)
Ratea (no.
of events)
Ratea (no.
of events)
Ratea (no.
of events)
Hazard ratiob
(95% CI)
Hazard ratiob
(95% CI)
Hazard ratiob
(95% CI)
Men
0/0
1826
6.2 (87)
1.0
3.7 (52)
1.0
1.7 (24)
1.0
0/1–2
1145
7.6 (61)
1.23 (0.9–1.7)
5.4 (44)
1.47 (1.0–2.2)
3.2 (26)
1.90 (1.1–3.3)
1–2/1–2
694
9.4 (45)
1.52 (1.1–2.2)
4.9 (24)
1.34 (0.8–2.2)
2.2 (11)
1.31 (0.6–2.7)
0–2/3+
900
11.6 (66)
1.87 (1.4–2.6)
6.7 (38)
1.82 (1.2–2.8)
3.2 (20)
1.94 (1.1–3.5)
3+/3+
456
15.4 (42)
2.49 (1.7–3.7)
7.5 (19)
2.03 (1.2–3.5)
4.7 (11)
2.81 (1.4–5.8)
(301)
2.80c (1.9–4.2)
(177)
2.14c (1.3–3.6)
(92)
2.50c (1.2–5.2)
RII
5021
P-value for RII
0.001
0.001
0.001
a Age adjusted event rates per 1000 person years.
b Age adjusted hazard ratio and 95% confidence intervals.
c Hazard ratio for the relative index of inequality (RII) represents the ratio of the incident coronary event rate for those with the greatest economic difficulties
compared with those with the least.
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Table 1 shows that the greater the economic difficulties score,
the higher the incidence of coronary events. The RII shows that
men with the greatest economic difficulties score had 2.5 times
higher incidence of MI than men with the lowest difficulties
(P 0.001). Similar trends are seen for MI plus definite angina
and total coronary events (P 0.001). The hazard ratios for the
persistent difficulties score categories confirm the usually
monotonically increasing gradient in coronary events.
The association between economic difficulties and other risk
factors for heart disease are shown in Table 2. A strong inverse
association is observed between economic difficulties and age.
Strong positive associations are seen between economic
difficulties and both rented housing and low employment grade
with a non-linear association between economic difficulties and
car access (P-value for heterogeneity, P = 0.03).
factors. The hazard ratio for the model adjusted for age,
socioeconomic and early life factors is taken as the baseline. This
model still shows strong associations between economic
difficulties and coronary events. Psychosocial work
characteristics and health-related behaviours only have a small
effect on the RII for the three event outcomes, while adjustment
for all the biological factors attenuates the association by 19%
in the case of MI and MI plus definite angina, and by 11% for
total coronary events.
Adjusting for all the potential explanatory variables
simultaneously attenuates the RII by 16% for MI, 24% for MI
plus definite angina and 17% for total coronary events.
ECONOMIC DIFFICULTIES AND CORONARY EVENTS
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Table 2 Associationsa between economic difficulties and other risk factors at Phase 3, in 5021 men
Economic difficulties score
Coronary risk factor
0/0
Percentage or
mean (SE)
0/1–2
Percentage or
mean (SE)
1–2/1–2
Percentage or
mean (SE)
0–2/3+
Percentage or
mean (SE)
3+/3+
Percentage or
mean (SE)
Test for
trend
50.1 (0.1)
49.1 (0.2)
48.8 (0.2)
48.2 (0.2)
47.8 (0.3)
P 0.001
3.9 (0.5)
4.8 (0.6)
4.3 (0.8)
5.73 (0.8)
10.0 (1.4)
P 0.001
11.3 (0.8)
10.3 (0.9)
8.3 (1.0)
9.4 (1.0)
14.6 (1.7)
P = 0.81
3.5 (0.4)
5.7 (0.7)
6.8 (1.0)
8.3 (1.0)
16.9 (2.0)
P 0.001
Age
Socioeconomic measures
Household tenure (rented) (%)
No car access (%)
Low employment grade (%)
Early life factors
Father manual social classb (%)
42.3 (1.5)
39.0 (2.0)
38.7 (1.8)
40.1 (2.6)
P = 0.68
176.6 (0.2)
176.1 (0.2)
176.1 (0.2)
176.1 (0.3)
P = 0.003
High alcohol consumption (%)
17.6 (0.9)
19.2 (1.2)
16.9 (1.4)
18.8 (1.3)
16.7 (1.8)
P = 0.95
Little exercise (%)
13.5 (0.8)
12.1 (1.0)
11.6 (1.2)
16.4 (1.3)
18.4 (1.9)
P = 0.01
8.1 (0.6)
12.8 (1.0)
12.8 (1.3)
13.0 (1.1)
20.5 (1.9)
P 0.001
122.2 (0.3)
121.2 (0.4)
121.8 (0.5)
122.2 (0.4)
121.8 (0.6)
P = 0.88
Health related behaviours
Current cigarette smoker (%)
Biological factors
Systolic blood pressure (mm Hg)
Diastolic blood pressure (mm Hg)
Cholesterol (mmol/litre)
80.9 (0.2)
80.5 (0.3)
81.1 (0.4)
81.6 (0.3)
81.4 (0.4)
P = 0.03
6.42 (0.03)
6.47 (0.03)
6.42 (0.04)
6.48 (0.04)
6.48 (0.05)
P = 0.19
LDL (mmol/litre)
4.41 (0.02)
4.45 (0.03)
4.42 (0.04)
4.45 (0.03)
4.43 (0.05)
P = 0.51
HDL (mmol/litre)
1.34 (0.01)
1.33 (0.01)
1.31 (0.01)
1.30 (0.01)
1.27 (0.02)
P = 0.002
Triglyceride (mmol/litre)
1.48 (0.03)
1.56 (0.04)
1.57 (0.05)
1.61 (0.04)
1.88 (0.06)
P 0.001
Fibrinogen (g/litre)
Waist-hip Ratio
2.32 (0.01)
2.34 (0.02)
2.32 (0.02)
2.34 (0.02)
2.35 (0.03)
P = 0.18
0.894 (0.001)
0.898 (0.002)
0.904 (0.002)
0.909 (0.002)
0.912 (0.003)
P 0.001
24.7 (0.1)
25.0 (0.1)
25.2 (0.1)
25.4 (0.1)
25.5 (0.1)
P 0.001
0.20 (0.02)
0.23 (0.02)
0.22 (0.03)
0.28 (0.03)
0.37 (0.04)
P 0.001
0.7 (0.2)
0.5 (0.2)
1.3 (0.4)
1.4 (0.4)
1.3 (0.6)
P = 0.02
BMI (kg/m2)
Insulin resistance (HOMA units)
Diabetic (%)
Psychosocial characteristics
Low job control (%)
16.6 (0.9)
18.7 (1.2)
17.0 (1.5)
22.0 (1.5)
33.0 (2.4)
P 0.001
High job demands (%)
36.9 (1.2)
36.0 (1.5)
32.9 (1.9)
30.1 (1.6)
32.9 (2.5)
P = 0.005
Low work support (%)
29.3 (1.1)
34.0 (1.5)
33.1 (1.9)
37.1 (1.8)
44.2 (2.5)
P 0.001
High effort/reward ratio (%)
32.8 (1.2)
31.8 (1.4)
34.7 (1.9)
35.0 (1.7)
38.9 (2.4)
P 0.001
a All associations, except age, are age-adjusted.
b Determined at baseline (Phase 1).
socioeconomic position in adulthood.27,28 Two of the three
markers of adult socioeconomic position, low employment
grade and housing tenure, were highly correlated with
economic difficulties. However, adjustment for these measures
produced little attenuation of the RII in economic difficulties
for any of the coronary event outcomes. Extensive work in
the Whitehall II study has documented employment grade
gradients in coronary events and coronary risk factors.13,29
Although both employment grade and economic difficulties are
markers of socioeconomic position, employment grade is
primarily a work-based measure. That the association between
economic difficulties and coronary events survives adjustment
for employment grade indicates that our measure of persistent
economic difficulties partly reflects a different set of factors than
those captured by measures of job hierarchy. It also includes a
contemporaneous measure of spending power amongst those
who were no longer in paid employment at Phase 3.
Furthermore, as indicated at the beginning of the article,
different measures of social position tap into different
dimensions of inequality and so adjustments of social gradients
in coronary events for potential confounders and mediating
factors will tend to produce different findings. In other analyses
we have shown that the household wealth gradient for minor
psychiatric morbidity and self-rated health in men remains
highly significant after adjustment for employment grade.30
Other studies have documented independent effects on
health for different markers of socioeconomic position.9,31,32
The Helsinki Health study, which used data from middle-aged
municipal employees, examined associations between seven
measures of socioeconomic position and self-rated health. It
was found that the association of economic difficulties in
adulthood with health was independent of the conventional
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38.4 (1.2)
176.8 (0.2)
Height (cm)
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Table 3 Association between the relative index of inequality (RII) for economic difficulties and coronary events in 5021 men, after adjusting for
other coronary risk factors
Adjustments
Total coronary events
MI plus definite angina
MI
Hazard Ratioa for
RII (95% CI)
Hazard Ratioa for
RII (95% CI)
Hazard Ratioa for
RII (95% CI)
% change
in RIIb
% change
in RIIb
% change
in RIIb
2.80 (1.9–4.2)
2.14 (1.3–3.6)
2.50 (1.2–5.2)
2.67 (1.7–4.1)
2.13 (1.2–3.7)
2.38 (1.1–5.1)
Age + SES measures + early life factorsd
2.70 (1.8–4.1)
Baseline
2.15 (1.2–3.7)
Baseline
2.39 (1.1–5.2)
Baseline
Age + SES measures + early life factors +
psychosocial work characteristicse
2.56 (1.7–3.9)
6
2.09 (1.2–3.7)
4
2.49 (1.1–5.4)
+5
Age + SES measures + early life factors +
health-related behavioursf
2.60 (1.7–4.0)
4
2.05 (1.2–3.6)
6
2.29 (1.0–5.0)
5
Age + SES measures + early life factors +
biological factorsg
2.42 (1.6–3.7)
11
1.86 (1.1–3.2)
19
2.02 (0.9–4.4)
19
Allh
2.28 (1.5–3.5)
17
1.78 (1.0–3.1)
24
2.07 (0.9–4.5)
16
a Hazard ratio for the RII represents the ratio of the incident coronary event rate for those with the greatest economic difficulties compared with those with
the least.
b Percentage changes in RII are calculated using the logarithm of the hazard ratio for the relative index of inequality.
c Socioeconomic status (SES) measures, housing tenure, car access and employment grade.
d Early life factors, father’s social class and height.
e Psychosocial work characteristics, job control, job demands, social support at work and effort-reward imbalance.
f Health-related behaviours, alcohol, exercise and smoking.
g Biological factors, systolic and diastolic blood pressure, cholesterol, HDL cholesterol, triglycerides, fibrinogen, waist-hip ratio, BMI, insulin resistance (HOMA
units) and diabetes.
h All = age + SES measures + early life factors + psychosocial work characteristics + health-related behaviours + biological factors.
indicators of socioeconomic position: education, occupation and
income. Economic difficulties were measured using the same
questions on food, clothing and bills as used in the present
study (P. Martikainen unpublished work). Few studies have
documented, or attempted to explain, associations between
economic difficulties and coronary events or coronary risk
factors using data from individuals. In the present study we
examined the potential explanatory power of three sets of
factors; psychosocial, behavioural and biological.
Job strain, in particular low control at work, and effortreward imbalance have been shown previously to be strong
determinants of the employment grade gradient in self-reported
coronary events among men in the Whitehall II study19,20,29,33
and other cohort studies.34,35 In the present study all the
psychosocial characteristics of the work environment were
highly correlated with economic difficulties, but contributed
little to the explanation of this gradient in coronary events. In
the case of effort-reward imbalance this result was particularly
unexpected as the effort-reward measure includes a financial
dimension. As all the psychosocial factors measured in this
study are work-based they are more likely to contribute to
grade gradients in coronary events than gradients related to
economic difficulties, as these operate away from the workplace.
Similarly it is possible that, in common with employment
grade,36 other work-based measures will lose some of their
strength as predictors of health once people have left
employment.
Adjustment for health-related behaviours attenuated the
economic difficulties gradient for each of the coronary event
outcomes by ~5%. The strong association between economic
difficulties and smoking suggests that part of the residual
association could be the result of the inaccuracy inherent in
using a single measurement as a proxy measure of lifetime
exposure to this CHD risk behaviour. This may lead to an
underassessment of the contribution of smoking as an
underlying mechanism in explaining social inequality in
CHD.2,37 However, in these data a more precise measure using
pack years explained no more of the gradient than the measure
used in these analyses. Smoking prevalence at Phase 3 was
only 13% overall, thus most men were non-smokers and the
majority of coronary events will have occurred in non-smokers.
In a population based sample of Finnish men adjustment of
the income gradient in coronary events for smoking, alcohol,
and exercise resulted in a greater attenuation of the
association.6 However, the prevalence of smoking and other
health-damaging behaviours may have been greater in this
population than among men in the Whitehall II study.
Furthermore, we have computed percentage changes in RII
using the logarithm of the hazard ratio for the RII rather than
the proportion of excess risk explained by other factors. This
method tends to give lower percentage changes compared with
using excess risks.
Adjustment for the biological factors measured in this study
attenuated associations between economic difficulties and
coronary events by 11–19%. These percentages seem relatively
low given that the health effects of socioeconomic position must
ultimately be understandable in terms of biological processes
occurring at the individual level. However, studies of income
gradients in coronary events have also documented excess risks
only partially explained by known behavioural and biological
risk factors.4,6,38 Studies of other markers of socioeconomic
position have also reported similar findings,1,37,39 although
other studies have found the well-known behavioural and
biological coronary risk factors to explain a considerable
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Age
Age + SES measuresc
ECONOMIC DIFFICULTIES AND CORONARY EVENTS
proportion of the socioeconomic gradient in coronary
events.40,41
Methodological considerations
then any differences in the strength of the association between
total coronary events and MI plus definite angina may be due
to reporting bias. Furthermore, if all of this difference is
attributable to reporting bias then the overestimate is 26%.
However, the gradient based on events with full clinical
verification is still 2.14 and highly significant. A further possible
source of bias relates to non-response. In common with other
studies, response in this study was lower in the lower
employment grades. Non-responders are more likely to have
economic difficulties and be at greater risk of coronary events
than responders. The theoretical inclusion of non-responders
in analyses thus would probably make the distribution of
economic difficulties more even and would also be likely to
increase the RII for coronary events.
In addition to non-response and unmeasured explanatory
variables we must also consider the role that imprecise
measurement of correlated risk factors might have played in
biasing our observed excess risk.46 It has been shown that the
direction and magnitude of such bias is unpredictable in
multivariate analyses,47 so it is possible that the magnitude of
the excess risk remaining after adjustment may be due in part
to imprecisely measured risk factors. While measurement of
biological risk factors in the Whitehall II study is executed
using standardized protocols with frequent quality control
checking,48 measurement error cannot be discounted.
Conclusion
We have demonstrated an economic difficulties gradient in
coronary events in men that is independent of other markers
of socioeconomic position and appears to be only partially
mediated by well-known risk factors in mid-life. Public health
recommendations arising from this work are that policies
ensuring households can meet their bills and afford adequate
food and clothing should be considered. The experience of
economic difficulties represents a dimension of socioeconomic
inequality that has been largely neglected in social
epidemiology. As a consequence the causes underlying the
association between economic difficulties and coronary events
and other health outcomes remain largely unknown.
Acknowledgements
Sources of funding: the Whitehall II study has been supported by
grants from the Medical Research Council; British Heart
Foundation; Health and Safety Executive; Department of Health;
National Heart Lung and Blood Institute (HL36310), US, NIH:
National Institute on Aging (AG13196), US, NIH; Agency for
Health Care Policy Research (HS06516); and the John D and
Catherine T MacArthur Foundation Research Networks on
Successful Midlife Development and Socio-economic Status and
Health. J.E.F. was supported by the Medical Research Council
(Grant no. 47413) during the preparation of this paper. P.M. is
supported by the Academy of Finland (Grant nos 70631 and
48600). M.J.S. is supported by the British Heart Foundation.
M.M. is supported by an MRC Research Professorship. We thank
all participating Civil Service departments; the Occupational
Health and Safety Agency; the Council of Civil Service Unions;
all participants in the Whitehall II study; and all members of the
Whitehall II study team.
Downloaded from http://ije.oxfordjournals.org/ at Pennsylvania State University on September 13, 2016
The present findings are only for men, all nominally white-collar
civil servants on entry to the study. However, generalizability
may be less limited than first imagined. Household income and
low control at home are stronger determinants of health for
women than men in the Whitehall II cohort.30,42 Thus, we
expect to see these findings replicated in women when there are
sufficient events for analysis. Furthermore, participants covered
a wide range of employment grades with annual full-time
salaries in 1995 ranging from £4995 to £150 000.
Of obvious concern in our analyses is the potential bias
caused by inability to adjust for income. Unfortunately data on
income were not collected in the Whitehall II study before
Phase 5 (1997–99) as personal income in the British Civil
Service was closely tied to employment grade, at least until the
mid-1990s.43 To evaluate this potential residual confounding
bias, we analysed the associations between household income
at Phase 5 and economic difficulties at earlier phases. These
analyses show that the correlation between household income
at Phase 5 and the economic difficulties score between Phases 1
and 3 is weak, i.e. 0.11.
Further, to evaluate the extent of unmeasured income bias,
we compared the longitudinal relationship between our
persistent economic difficulties measure and self-rated health
at Phase 5 with the cross-sectional relationship between
household income and self-rated health at Phase 5. When
entered into the same age-adjusted Cox regression model both
measures demonstrated an independent association with poor
self-rated health. The RII (95% CI) for economic difficulties was
2.30 (1.6–3.3) and that for household income was 2.58
(1.7–3.3), indicating strong independent effects for the two
measures. It is thus very unlikely—because of weak correlations
between economic difficulties and income, and independent
associations of the two measures with another health outcome—
that failure to adjust for income could be driving the association
between economic difficulties and coronary events.
However, non-measurement of further socioeconomic and
other explanatory factors may complicate the interpretation of
our analyses. Such covariates include education, heritability,
pre-clinical disease, perception of symptoms and diagnosis,
ethnicity, diet, processes involving homocysteine, infection, and
inflammation. A question on education was included part-way
through the baseline screening of the Whitehall II cohort.
However, exclusion of participants with these data missing
would have unduly restricted the number of men included in
the analyses. In addition to the covariates already discussed,
economic difficulties may also reflect psychosocial determinants
of health outside work, such as lack of control over life, low
social integration in the local neighbourhood, and a local social
infrastructure that does not allow full social engagement and
participation in society.44,45
Our composite economic difficulties measure and 41% (124)
of our total coronary events were derived from self-reported
data only. For these 124 events there is the possibility that
reporting bias has led to an overestimate of the association with
economic difficulties. If we assume that the magnitude of the
effects of economic difficulties on MI and angina are the same,
7 of 9
8 of 9
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
KEY MESSAGES
•
There is a steep graded association between economic difficulties and coronary events in men, which is independent of other
markers of socioeconomic position.
•
Less than one-quarter of this gradient appears to be accounted for by traditional coronary risk factors.
•
The experience of economic difficulties represents a dimension of socioeconomic inequality largely neglected in social
epidemiology. Consequently, the causes underlying the association between economic difficulties and coronary events and
other health outcomes remain largely unknown.
17 Friedewald WT, Levy RI, Fredrickson DS. Estimation of the con-
1 Marmot MG, Rose G, Shipley M, Hamilton PJS. Employment grade
centration of low-density lipoprotein cholesterol in plasma, without use
of the preparative ultracentrifuge. Clin Chem 1972;18:499–502.
and coronary heart disease in British civil servants. J Epidemiol
Community Health 1978;32:244–49.
18 Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF,
2 Bucher HC, Ragland DR. Socioeconomic indicators and mortality
from coronary heart disease and cancer—a 22 year follow-up of
middle-aged men. Am J Pub Health 1995;85:1231–36.
3 Elo IT, Preston SH. Educational differentials in mortality—United
States, 1979–85. Soc Sci Med 1996;42:47–57.
Turner RC. Homeostasis model assessment: insulin resistance and
beta-cell function from fasting plasma glucose and insulin
concentrations in man. Diabetologia 1985;28:412–19.
19 Kuper H, Marmot M. Job strain, job demands, decision latitude,
and risk of coronary heart disease within the Whitehall II study.
J Epidemiol Community Health 2003;57:147–53.
4 Smith GD, Neaton JD, Wentworth D, Stamler R, Stamler J.
20 Kuper H, Singh-Manoux A, Marmot M. When reciprocity fails: effort-
Socioeconomic differentials in mortality risk among men screened for
the Multiple Risk Factor Intervention Trial: I. White men. Am J Public
Health 1996;86:486–96.
reward imbalance in relation to coronary heart disease and health
functioning within the Whitehall II study. Occup Environ Med
2002;59:777–84.
5 Chandola T. Social inequality in coronary heart disease: a comparison
21 Rose GA, Blackburn H, Gillum RF, Prineas RJ. Cardiovascular Survey
of occupational classifications. Soc Sci Med 1998;47:525–33.
Methods, 2nd edn. Geneva: WHO, 1982.
6 Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT. Do
22 Prineas RJ, Crown RS, Blackburn H. The Minnesota Code Manual of
cardiovascular risk factors explain the relation between
socioeconomic status, risk of all-cause mortality, cardiovascular
mortality, and acute myocardial infarction? Am J Epidemiol
1996;144:934–42.
Electrocardiographic Findings: Standards and Procedures for Measurement
and Classification. Bristol, UK: John Wright, 1982.
23 Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas
Socioeconomic factors, health behaviours, and mortality. JAMA
1998;279:1703–08.
AM, Pajak A. Myocardial infarction and coronary deaths in the world
health organization MONICA project. Registration procedures, event
rates, and case-fatality rates in 38 populations from 21 countries in
four continents. Circulation 1994;90:583–612.
8 Bartley M, Sacker A, Firth D, Fitzpatrick R. Understanding social
24 Rose G, Hamilton PS, Keen H, Reid DD, McCartney P, Jarrett RJ.
variation in cardiovascular risk factors in women and men: the
advantage of theoretically based measures. Soc Sci Med
1999;49:831–45.
Myocardial ischaemia, risk factors and death from coronary heartdisease. Lancet 1977;1:105–09.
7 Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J.
9 Lahelma E, Martikainen P, Laaksonen M, Aittomaki A. Pathways
between socioeconomic determinants of health. J Epidemiol
Community Health 2004;58:327–32.
10 Lynch JW, Kaplan GA. Socioeconomic position. In: Berkman LF,
Kawachi I (eds). Social Epidemiology. Oxford: Oxford University Press,
2000, pp. 13–35.
11Fritzell J, Nermo M, Lundberg O. The impact of income: assessing the
relationship between income and health in Sweden. Scand J Public
Health 2004;32:6–16.
12 Office of National Statistics. Family Spending: A report on the 2002–2003
Expenditure and Food Survey. London: TSO, 2004.
13 Marmot MG, Davey Smith G, Stansfeld S et al. Health inequalities
among British civil servants: the Whitehall II study. Lancet
1991;337:1387–93.
14 Pearlin LI, Schooler C. The structure of coping. J Health Soc Behav
1978;19:2–21.
15 OPCS Social Survey Division. General Household Survey 1982. London:
HMSO, 1984.
25 Kunst AE, Mackenbach JP. The size of mortality differences associated
with educational level in nine industrialised countries. Am J Pub
Health 1994;84:932–37.
26 Schafer JL. Analysis of Incomplete Multivariate Data. London: Chapman
and Hall, 1997.
27 Ben-Shlomo Y, Davey Smith G. Deprivation in infancy or adult life:
which is more important for mortality risk? Lancet 1991;337:530–4.
28 A Life Course Approach to Chronic Disease Epidemiology. Kuh D, Ben-
Shlomo B (eds). Oxford: Oxford University Press, 2004.
29 Marmot MG, Bosma H, Hemingway H, Stansfeld S. Contribution of
job control and other risk factors to social variations in coronary heart
disease incidence. Lancet 1997;350:235–39.
30 Martikainen P, Adda J, Ferrie JE, Davey Smith G, Marmot M. The
effects of income and wealth on GHQ depression and poor self-rated
health in white-collar women and men in the Whitehall II study.
J Epidemiol Community Health 2003;57:718–23.
31 Dahl E. Social inequalities in ill-health—the significance of
occupational status, education and income—results from a Norwegian
survey. Soc Health Illn 1991;16:492–505.
16 Brunner EJ, Marmot MG, Nanchanlal K et al. Social inequality in
32 Sorlie PD, Backlund E, Keller JB. US mortality by economic,
coronary risk: central obesity and the metabolic syndrome. Evidence
from the Whitehall II study. Diabetologia 1997;40:1341–49.
demographic, and social characteristics: the National Longitudinal
Mortality Study. Am J Public Health 1995;85:949–56.
Downloaded from http://ije.oxfordjournals.org/ at Pennsylvania State University on September 13, 2016
References
ECONOMIC DIFFICULTIES AND CORONARY EVENTS
9 of 9
33 Bosma H, Marmot MG, Hemingway H, Nicholson A, Brunner EJ,
40 Pekkanen J, Tuomilehto J, Uutela A, Vartiainen E, Nissinen A. Social
Stansfeld S. Low job control and risk of coronary heart disease
in the Whitehall II (prospective cohort) study. BMJ 1997;314:
558–65.
class, health behaviour, and mortality among men and women in
eastern Finland. BMJ 1995;311:589–93.
34 Kuper H, Marmot M, Hemingway H. Systematic review of prospective
cohort studies of psychosocial factors in the etiology and prognosis
of coronary heart disease. Semin Vasc Med 2002;2:267–314.
35 Hemingway H, Marmot M. Evidence based cardiology: psychosocial
factors in the aetiology and prognosis of coronary heart disease.
Systematic review of prospective cohort studies. BMJ
1999;318:1460–67.
36 Marmot MG, Shipley MJ. Do socioeconomic differences in mortality
41 Strand BH, Tverdal A. Can cardiovascular risk factors and lifestyle
explain the educational inequalities in mortality from ischaemic heart
disease and from other heart diseases? 26 year follow up of 50,000
Norwegian men and women. J Epidemiol Community Health
2004;58:705–09.
42 Chandola T, Kuper H, Singh-Manoux A, Bartley M, Marmot M. The
effect of control at home on CHD events in the Whitehall II study:
Gender differences in psychosocial domestic pathways to social
inequalities in CHD. Soc Sci Med 2004;58:1501–09.
persist after retirement? 25 Year follow up of civil servants from the
first Whitehall II study. BMJ 1996;313:1177–80.
43 H.M.Treasury. Pocket Pay Guide 1995 Settlements. London: H.M
37 Pocock SJ, Shaper AG, Cook DG, Phillips AN, Walker M. Social class
44 MacIntyre S, Ellaway A, Cummins S. Place effects on health: how can
differences in ischaemic heart disease in British men. Lancet
1987;2:197–201.
we conceptualise, operationalise and measure them? Soc Sci Med
2002;55:125–39.
and death due to coronary heart disease and any disease: a
longitudinal study in eastern Finland. J Epidemiol Community Health
1982;36:294–97.
39 Vartiainen E, Pekkanen J, Koskinen S, Jousilahti P, Salomaa V,
Puska P. Do changes in cardiovascular risk factors explain
the increasing socioeconomic difference in mortality from ischaemic
heart disease in Finland? J Epidemiol Community Health 1998;
52:416–19.
45 Marmot M. Status Syndrome. London: Bloomsbury, 2004.
46 Phillips AN, Davey Smith G. Bias in relative odds estimation due to
imprecise measurement of correlated exposures. Stat Med 1992;11:953–61.
47 Liu K. Measurement error and its impact on partial correlation and
multiple linear regression analysis. Am J Epidemiol. 1988;127:864–74.
48 Beksinska M, Yea L, Brunner E. Whitehall II study: Manual for Screening
Examination 1991–93. London: Department of Epidemiology and
Public Health, University College London, 1995.
Downloaded from http://ije.oxfordjournals.org/ at Pennsylvania State University on September 13, 2016
38 Salonen J. Socioeconomic status and risk of cancer, cerebral stroke,
Treasury PSPP Division, 1995.