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Online supplement
Demographics of countries of the International Cancer
Benchmarking Partnership
Supplementary Table 1 Demographics of populations aged 50+ of countries/regions
taking part in International Cancer Benchmarking Partnership
Australia
Canada
Denmark
(New
South
Wales,
Victoria)
(Alberta,
British
Columbia,
Manitoba,
Ontario)
Women (%)
52.4
52.2
52.5
Aged 70+ (%)
31.7
27.6
Educated to Bachelor’s
degree level (%)
14.4
21.7
Norway
Sweden
UK
(UppsalaÖrebro,
StockholmGotland)
(England,
Northern
Ireland,
Wales)
52.3
52.6
53.2
30.6
31.2
32.9
34.2
27.1
23.3
30.1
14.9
Sources:
Australia
Bureau of Statistics Census 2006 (mid-2011 population estimates)
Canada
Statistics Canada (2006 census)
Denmark
Statistics Denmark (2010 data)
Norway
Statistics Norway and Eurostat (2010 data)
Sweden
Statistics Sweden (2010 data)
United Kingdom
Office for National Statistics Census 2001 (mid-2010 population
estimates)
Northern Ireland Statistics and Research Agency Census 2001 (mid-2010
population estimates)
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Weighting methods
The aim of weighting was to make estimates as representative as possible of the target
population. In this study, the target population included people aged 50+ of the populations
within participating regions (jurisdictions) of each country (Australia: New South Wales and
Victoria; Canada: Alberta, British Columbia, Manitoba, Ontario; all of Denmark; Sweden;
Uppsala-Örebro, Stockholm-Gotland; all of Norway; and the UK: England, Northern Ireland and
Wales.
Two forms of weights were applied to the survey results: design weights required because of
the survey design, and non-representativeness weights required to correct for different
proportions of people in subsets of the population in the sample population compared with the
target population:

design weights remove the sampling bias which results from use of varying selection
probabilities: they were applied in all countries except Denmark, Sweden and Norway
where all sampled individuals had an equal chance of selection;

non-representativeness weights were applied to reduce the level of nonrepresentativeness of the samples, for all jurisdictions.
Both design and non-response weights were applied to generate estimates representative of the
population of each jurisdiction on key variables.
Design weights
All countries except those where we sampled individuals directly from population registers (i.e.
except Denmark, Sweden and Norway) required the data to be weighted for design effects, in
other words, to correct for unequal probabilities of participation in different groups.
Design weights calculated for England, Wales, Northern Ireland, Canada (Alberta, British
Columbia, Manitoba, and Ontario), New South Wales and Victoria correct for unequal
probabilities of selection within households. The design weight applied is equal to the number of
eligible adults living in the household. For example, where there were two eligible adults living at
a sampled household, each adult had half the chance of selection of adults in single-person
households. Respondents from households where 2 adults were eligible for the survey are
therefore given a weight of 2.
When calculating the design weights for the UK (England, Wales and Northern Ireland), Canada
(Alberta, British Columbia, Manitoba, and Ontario), and Australia (New South Wales and
Victoria), we took into account the proportion of the population that lived within each of the
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jurisdictions. For example, in Australia (New South Wales and Victoria), 42.72% of the
population live in Victoria. The design weight for Victoria was therefore calculated as the total
number of eligible adults living in the household multiplied by 42.72%.
The population proportions used were

Australia: Victoria 42.72%, New South Wales 57.28%

Canada: British Columbia 16.96%, Alberta 16.67%, Ontario 13.18% and Quebec 53.20%

UK: England 91.36%, Wales 5.78% and Northern Ireland 2.86%
These were based on the latest available population estimates from the Australia Bureau of
Statistics, Statistics Canada, and the UK Office for National Statistics.
Non-representativeness weights
Non-representativeness weights were applied in all countries in an effort to lessen the bias
caused by non-representativeness of the participants. The weights were calculated by adjusting
the profile of the achieved sample to match the population profile on a key set of demographic
variables.
We collected population information for each country for age, gender, region, marital status,
highest level of education and ethnicity/ancestry (where available). We compared the sample
distributions for these variables with the population data. For variables where there were large
differences between the population and achieved sample, and where analysis of the data
showed these variables to be correlated with the key survey measures, we considered applying
non-representativeness weights.
In practice, the key demographic variables we considered – age, gender, marital status,
education level and ethnicity/ancestry – were often correlated with cancer awareness and beliefs
variables. Leaving the data unweighted therefore could have biased the overall estimates.
For example, in England, the Office for National Statistics mid-2009 population estimates show
that 53.2% of the population aged 50+ is female. However, 62.3% of our achieved sample was
female. If we did not apply non-response weights, the estimate would over-represent the views
of women (and, therefore, under-represent the views of men). Women are ‘weighted down’ (i.e.
given a weight <1) and men ‘weighted up’ so that, after applying the weighting, female
responses account for 53.6% of the aggregate total.
Non-representativeness weighting can only ensure that sample and population profiles match on
variables for which we have information about the interviewed sample. Table A2 provides the
data sources and variables used in each country.
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Supplementary table 2: Sources used to apply non-representativeness weights and
variables that were used in weighting
Sources
Population characteristics for which nonrepresentativeness weights applied
Australia
Bureau of Statistics Census 2006 (2010
population estimates)
Age/sex; education; metropolitan/rural residence;
country of birth
Canada
Statistics Canada (2006 census)
Age/sex; education; ancestry
Denmark
Statistics Denmark (2010 data)
Age/sex; region
Norway
Statistics Norway and Eurostat (2010 data)
Age/sex; region; marital status; education; country
of origin
Sweden
Statistics Sweden (2010 data)
Age/sex; region; education; country of origin
UK
Office for National Statistics Census 2001
(mid-2010 population estimates)
Northern Ireland Statistics and Research
Agency Census 2001 (mid-2010 population
estimates)
England: Age/sex; education
Northern Ireland: Age/sex; region; marital status;
education
Wales: Age/sex; region, marital status; education
Example of weighting methods
Assume that we have two weighting variables (e.g. age and ancestry). The method used
was:

Weights were calculated to align the achieved sample with the marginal population
distribution on the first variable.

The weights were applied. A new marginal distribution was formed for the second
weighting variable.

The w process was repeated for the second variable, with new weights applied to
align the achieved sample with the marginal distributions for the second variable.

The weighted sample would then generally be misaligned with the first weighting
variable again, so the whole cycle is repeated.

The process was repeated until the sample distribution closely matched the
population distribution

The non-representativeness weights were then calculated as the ratios of the final to
the original sample proportions of each weighting sub-class.
The weighting variables were selected by comparing the design-weighted data to the population
profiles for key variables (in each country these were: age x gender, region, education level,
ethnicity/ancestry, marital status). All these variables varied by several survey measures, and so
where there were significant differences between the achieved sample profile and population, we
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considered applying weights. The weights were applied sequentially, to check whether weighting
one demographic variable might align profiles for non-weighted variables.
We used a rim weighting approach (also known as raking). This method is used when we are
weighting to several demographic profile characteristics, but do not know how all characteristics
interconnect (i.e. we know only the marginal distributions, and not the interlocking distributions
for the population for age, marital status, etc.). The technique works by aligning the marginal
distributions of the achieved sample to the population profiles while making as little impact as
possible to the pattern of the interlocking distributions.
Final weights
The final weights were calculated by multiplying the design weight by the non-representativeness
weight for each individual.
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