The chances of surviving a given cancer are not the same for all patients in all regions
of England (Yuen et al, 1997; Coleman et al, 1999). Regional differences in survival
within the UK have also been reported for several cancers (Silman and Evans, 1981;
Chouillet, Bell and Hiscox, 1994; Macleod et al, 1998). In particular, there is evidence
that, for most adult cancers, patients from affluent neighbourhoods have better survival
than patients from deprived neighbourhoods. Such differentials are most unlikely to
be attributable to chance (Kogevinas, 1990; Kogevinas et al, 1991; Schrijvers, 1996;
Kidd, 1997; Pollock and Vickers, 1997) or to the extent of disease at the time of
diagnosis (Carnon et al, 1994; Schrijvers et al, 1995a, 1995b). For breast cancer
patients, these differentials have been associated with variations in diagnostic investigations
both in England and Wales and in Scotland, and with departures from treatment guidelines
(Gillis and Hole, 1996; Richards et al, 1996; Twelves et al, 1998; Stockton, 2002).
Using detailed geographical survival data on breast cancer patients diagnosed in England
in 1992–1994, we have compared survival patterns across regions and investigated their
variation using demographic and socioeconomic indicators.
PARTICIPANTS AND METHODS
Participants
Incidence data for breast cancer in 1992–1994, submitted to the Office for National
Statistics by the nine regional cancer registries in England, were linked to death
and emigration data by the National Health Service Central Register (NHSCR). Data
were frozen in October 2000, when follow-up was considered acceptable up to 31 December
1999 (Coleman et al, 2000). A total of 93 687 records were included in the analysis.
Some (5368) records were declared ineligible as the tumour was either in situ, benign
or metastatic, or data were incomplete. Of the 88 319 eligible records, 11% were later
excluded from the analysis, mainly for one of three reasons: survival time could not
be calculated because only the date of death was known (death certificate only, 5341),
a previous primary malignancy (1860), or synchronous tumours (872), or for lack of
reliable information from NHSCR about vital status when the data were frozen (812).
Details of these and other criteria, which accounted for the remaining 530 exclusions,
have been published (Coleman et al, 1999). After exclusions, a total of 78 904 breast
cancer patients, with age at diagnosis ranging from 16 to 99 years, were available
for analysis.
Statistical analysis
The 5-year relative survival rates were computed separately for each of the 99 Health
Authorities (HAs) in England (1999 boundaries). We used the age- and sex-specific
England and Wales life-tables for the 1990–1992 pericensal period and adapted a method
to estimate relative survival rates (Esteve et al, 1990).
The relative survival rates were plotted using a Geographical Information Systems
(GIS) map. To investigate their variation across the nine English regions, several
potential covariates were considered, in line with other studies (Quinn and Allen,
1995; Schrijvers et al, 1995; Coleman et al, 1999). These were all defined at the
HA level and referred to either summary statistics of patient characteristics (e.g.
mean age at diagnosis for each HA) or to summary statistics of enumeration district
(ED) characteristics derived from the 1991 Census (e.g. mean percentage of Asians
for every HA). They are listed in Table 1
Table 1
Summary statistics of the Health Authority (HA)-specific 5-year relative survival
rates for women diagnosed with breast cancer in 1992–1994 by categories of potential
explanatory variables
5-Year relative survival
Variables
Median
Interquartile range
Geographic
Region
London
77.44
75.63, 79.58
Eastern
78.04
76.92, 78.91
North West
74.03
72.12, 75.09
Northern and Yorkshire
74.38
71.92, 75.01
South East
75.95
73.94, 77.63
South West
74.53
72.72, 74.87
Trent
72.41
69.72, 74.35
West Midlands
75.39
74.08, 76.87
Demographic (grouped in thirds)
Mean age at diagnosis of breast cancer cases in HA
Low (58.8–)
75.76
73.76, 77.87
Medium (61.4–)
74.82
73.50, 78.21
High (62.3–66.6)
74.50
72.38, 75.51
Mean percentage of women aged 15–64 years among EDs in HA
a
Low (59.0–)
74.56
71.92, 75.39
Medium (63.1–)
74.47
72.38, 76.46
High (64.4–71.0)
76.70
74.70, 78.41
Mean percentage of Black Afro Caribbean among EDs in HA
a
Low (0.05–)
74.47
72.63, 76.44
Medium (0.28–)
74.87
73.96, 76.87
High (0.87–18.1)
75.76
72.83, 77.87
Mean percentage of Asians among EDs in HA
a
Low (0.06–)
74.56
72.76, 76.44
Medium (0.48–)
74.71
73.40, 77.88
High (2.25–18.7)
75.57
72.83, 77.69
Socioeconomic (grouped in thirds)
Mean percentage of social class IV or V heads of household among EDs in HA
a
Low (9.3–)
76.70
75.51, 78.88
Medium (16.4–)
74.98
73.96, 77.19
High (19.7–26.9)
72.41
69.72, 74.47
Mean percentage of unemployed among EDs in HA
a
Low (4.7–)
75.98
74.42, 77.91
Medium (6.9–)
75.33
72.71, 77.69
High (10.2–23.2)
73.76
71.85, 75.39
Carstairs index
a,b
Affluent (−2.6–)
76.52
74.71, 78.17
Medium (−0.2–)
74.82
73.18, 77.19
Deprived (1.8–10.4)
73.37
71.39, 75.51
Mean HA Townsend score among EDs in HA
a
Affluent (−5.5–)
76.66
74.71, 78.17
Medium (−2.4–)
74.55
72.76, 76.05
Deprived (1.0–12.2)
74.30
71.85, 76.40
Mean HA Jarman index among EDs in HA
a
Affluent (−27.7–)
76.44
74.57, 77.97
Medium (−9.0–)
74.49
72.71, 76.69
Deprived (4.5–61.4)
74.56
71.85, 76.40
a
Variable derived from 1991 Census data on ED characteristics.
b
Weighted mean of ED values.
.
In descriptive analyses, all variables were grouped into categories defined by the
tertiles of their distribution, but in regression models they were left as continuous
variables, centred on their means (by subtracting the mean value from each observation)
to obtain interpretable baseline rates. Fixed and random effects linear regression
models (as used in meta-analyses, Whitehead and Whitehead, 1991) were fitted to quantify
the variation in HA-specific relative survival rates and to identify the strongest
HA-level covariates. Robust estimates of precision were used with fixed effects models
in order to deal with the likely geographical correlation among the individual HAs.
By contrast, random effects models directly specify such correlations leading to estimates
of between-HA variances (τ
2). These are measures of the heterogeneity among HAs that is unaccounted for by the
covariates included in a model. Tests of significance and departure from linearity
of continuous effects were performed via likelihood ratio tests (Clayton and Hills,
1993). Multivariable fixed effects models were compared using the strategy recommended
by Collett (1994: pp 78–85), with P<0.10 as the inclusion criterion. The potential
confounding effect of mean age at diagnosis was examined by forcing it into the final
model.
Analyses were performed in Stata version 8 (StataCorp, 2003). Geographical Information
Systems maps were produced in Arcview (Arcview GIS. v3.1; Environmental Systems Research
Institute Inc.,).
RESULTS
Of the 78 904 women included, 27 532 (35%) died within 5 years of diagnosis. The mean
5-year relative survival rate was 75%, with the HA-specific values ranging from 66
to 85%. There was evidence of some clustering among adjacent HAs, likely to be due
to their sharing of a number of characteristics (Figure 1
Figure 1
Observed geographical variation in 5-year relative survival rates among the 99 England
and Wales Health Authorities (1999 boundaries): categories defined by the quartiles
of the distribution.
). Random effects meta-analysis without covariates provided evidence of a relatively
large and significant between-HA variance (estimated τ
2=8.47; P<0.001), thus supporting the visual impression of variability.
The distribution of 5-year relative survival rates by categories of the available
covariates is shown in Table 1. There is evidence of geographical similarities among
the Northern regions, with the relative survival rates in North West, Trent and Northern
and Yorkshire being on average considerably lower than in London and in the Eastern
and South East regions. Survival rates also decreased with greater mean age at diagnosis,
while it increased with higher mean proportion of younger (aged 15–64 years) women
living in the HA, the latter possibly being an indicator of greater affluence. There
was some evidence that HAs with a greater mean percentage of Black Afro-Caribbean
and Asians had higher relative survival rates. Health Authorities with higher mean
proportions of heads of household in lower social classes, or higher mean unemployment
rate, or higher deprivation indices showed strong negative trends in survival rates.
Univariable fixed effects regression analyses of these same factors, treated as continuous
variables, revealed that the socioeconomic and geographical indicators were the strongest
predictors of the 5-year survival rates (all P<0.01; Table 2
Table 2
Crude effects of potential explanatory variables for the HA 5-year relative survival
rates estimated using fixed and random effects regression models
Univariable fixed effects model
Univariable random effects model
Explanatory variable
Coefficient
95% CIa
P-value
Coefficient
95% CI
P-value
τ
2
Geographic
Region
London (baseline)
1
—
—
1
—
Eastern
1.124
(−0.962, 3.121)
0.29
1.045
(−1.475, 3.565)
0.416
North West
−3.300
(−5.620,−0.976)
0.006
−3.460
(−5.663,−1.258)
0.002
Northern and Yorkshire
−2.695
(−5.880, 0.489)
0.10
−3.238
(−5.541,−0.935)
0.006
5.76
South East
−0.352
(−2.787, 2.084)
0.77
−0.514
(−2.693, 1.665)
0.644
South West
−3.186
(−5.684,−0.688)
0.01
−3.092
(−5.624,−0.559)
0.017
Trent
−4.615
(−7.031,−2.198)
<0.001
−4.876
(−7.307,−2.444)
<0.001
West Midlands
−1.565
(−3.992, 0.862)
0.20
−1.745
(−4.082, 0.592)
0.143
Demographic
HA mean age at breast cancer diagnosis in 1992–1994
−0.433
(−0.895, 0.029)
0.06
−0.415
(−0.926, 0.097)
0.11
8.28
HA mean of ED percentages of women aged 15–64 years
0.360
(0.089, 0.631)
0.01
0.408
(0.073, 0.742)
0.02
7.91
HA mean of ED percentages of Black Afro Caribbean
0.053
(−0.174, 0.280)
0.65
0.069
(−0.164, 0.303)
0.56
8.55
HA mean of ED percentages of Asians
0.038
(−0.128, 0.205)
0.65
0.043
(−0.146, 0.234)
0.65
8.57
Socioeconomic
HA mean of ED percentages unemployed
−0.372
(−0.564,−0.180)
<0.001
−0.392
(−0.571,−0.212)
<0.001
6.64
HA mean of ED percentages of class IV and V households
−0.529
(−0.704,−0.353)
<0.001
−0.551
(−0.705,−0.397)
<0.001
4.44
HA mean of ED Carstairs indices
−0.637
(−0.960,−0.313)
<0.001
−0.683
(−0.971,−0.396)
<0.001
6.34
HA Townsend score
−0.300
(−0.512,−0.086)
0.006
−0.313
(−0.501,−0.125)
0.001
7.37
HA Jarman index
−0.065
(−0.107,−0.023)
0.003
−0.070
(−0.108,−0.032)
<0.001
7.14
). This is also shown by the largest reduction in estimated τ
2 corresponding to the random effects models that included these variables. None of
them showed evidence of departure from the null hypothesis of a linear effect (with
the exception of HA mean percentage of black Afro-Caribbean; P=0.03). Multivariable
fixed effects models revealed social class and region to be the most important factors
(Table 3
Table 3
Final fixed and random effects regression model with the significant predictors
Multivariable fixed effects model
Multivariable random effects model
Explanatory variable
Coefficient
95% CIa
P-value
Crude coefficient
95% CI
P-value
τ
2
Intercept
75.43
73.78, 77.07
75.42
73.87, 76.96
Region
London (baseline)
1
1
Eastern
1.691
−0.115, 3.497
0.066
1.595
−0.646, 3.836
0.163
North West
−0.879
−3.092, 1.334
0.432
−0.903
−3.129, 1.323
0.426
Northern andYorkshire
0.166
−3.018, 3.351
0.918
−0.112
−2.513, 2.290
0.927
South East
−0.454
−2.396, 1.488
0.643
−0.476
−2.415, 1.462
0.630
South West
−2.288
−4.496, −0.081
0.042
−2.105
−4.385, 0.175
0.070
Trent
−2.270
−4.443, −0.081
0.041
−2.205
−4.630, 0.220
0.075
West Midlands
0.550
−2.007, 3.107
0.670
0.419
−1.850, 2.688
0.718
HA mean of ED percentages of class IV and V households
−0.493
−0.707, −0.280
<0.001
−0.498
−0.697, −0.298
<0.001
3.87
). The mean age at diagnosis did not confound or modify these effects. Repeating the
analyses using random effects models confirmed these results and showed that the between-HA
variation was more than halved from 8.47 (corresponding to the data of Figure 1) to
3.87. The intercept in the final random effects model (75.42%, 95% CI, 73.87, 76.96%)
represents the estimated 5-year relative survival rate for women diagnosed in a London
HA with average percentage (i.e. 17.9%) of class IV and V households. The estimated
coefficient (−0.498, 95% CI −0.697, −0.298) instead represents the decrease in 5-year
relative survival rates expected in any region for every percentage increase in HA
mean percentage of class IV and V households. Similarly, the estimated coefficients
for each region represent the increases (or decreases) in rates relative to the rate
expected in a London HA, holding percentage of class IV and V households fixed.
DISCUSSION
Our findings show that the significant variation in breast cancer survival between
HAs in England can be partly explained by socioeconomic differentials between and
within regions. Although the observation of an association between breast cancer survival
and deprivation is well documented (Karjalainen, 1991; Schrijvers et al, 1995; Coleman
et al, 1999), our findings add quantitative estimates of both accountable and residual
variation between HAs.
Our results suffer from several limitations. Firstly, since the measures for deprivation
were aggregated at HA level from smaller units (EDs), the results rely on the assumption
that all the variables that determine survival rates are uniformly distributed within
each HA. If this assumption were incorrect, the estimated effects would be biased,
most probably towards the null hypothesis of no effect. This assumption of homogeneous
deprivation level within each HA may be more appropriate when the geographical areas
are small, but less so when the areas are as large as HAs. Secondly, population figures
and socioeconomic indicators were taken from the 1991 decennial census, and therefore
may not be accurate in portraying characteristics of the HA population throughout
the years covered by this study (1992–1999). More up-to-date administrative data from
official sources are becoming available at small-area level (e.g. income support recipients,
Carstairs, 2000). Unfortunately, these were not available to us at the time of analysis.
The analyses were carried out at HA level, but HA areas cannot be considered as ‘units
of performance’ in terms of breast cancer care. Thus, part of the unexplained geographical
variation in survival rates may be due to differences in health care, such as the
timing and extent of initial investigation, or type and departures from treatment
guidelines (Quinn and Allen, 1995), or to individual level variables, such as the
extent of disease at diagnosis (e.g. tumour grade and stage). These variables were
not available to us. When more individual and HA-level data become available, our
approach should be replicated to monitor improvements in the quality of detection
and care of breast cancer patients and to inform local public health interventions.