Example Output 1 – Incidences of Rare Sarcomas

Country Incidences of Rare Sarcomas 2010-2013 IncidencesEngland Chordoma 115

Ewing's sarcoma, Ewing's tumour, Extraskeletal Ewing tumour 206Osteosarcoma, NOS (C40., C41.) 299

Scotland Chordoma 15Ewing's sarcoma, Ewing's tumour, Extraskeletal Ewing tumour 28Osteosarcoma, NOS (C40., C41.) 34

Wales Chordoma 12Ewing's sarcoma, Ewing's tumour, Extraskeletal Ewing tumour 21Osteosarcoma, NOS (C40., C41.) 23

Table 2 - Incidences of Rare Sarcomas in the UK by Country

Table 1 - Incidences of Rare Sarcomas in the UK

Incidences of Rare Sarcomas 2010-2013 IncidencesChordoma 142Ewing's sarcoma, Ewing's tumour, Extraskeletal Ewing tumour 255Osteosarcoma, NOS (C40., C41.) 361

Example Output 2 – Haematological Cancer Incidences in Essex

Table 3 - Cancer Incidences in Essex (Note '-' indicates that a low count has been suppressed for SDC purposes)

Area Incidences of Hodgkin Lymphoma

Incidences of Non-Hodgkin Lymphoma

Incidences of Multiple myeloma

Incidences of Myelodysplastic Syndromes

Total number of incidences

NHS Basildon & Brentwood CCG - 54 22 12 96NHS Castle Point & Rochford CCG - 36 15 11 67NHS Mid Essex CCG 11 75 31 16 133NHS Southend CCG - 37 15 10 67NHS Thurrock CCG - 34 14 - 60Total for Mid and South Essex CCGs

34 236 97 56 418

Example Output 3 – Lower Super Output Area Summary Statistics

Table 4- Lower Super Output Summary Statistics

Lower Super Output Area

Population Mean Income per year (£)

Highest Earner Income per year (to nearest £1,000)

Percentage of Households with English as Main

Language

Number of Same-Sex

HouseholdsE01000232 1282 22852 434000 97.2% 3E01000351 1580 25400 752000 93.1% 12E01000402 1115 27389 600000 98.8% 5E01000498 1094 31729 1128000 92.5% 6E01000527 1871 29422 338000 94.7% 11E01000639 1432 26186 560000 93.6% 8

Example Output 4 – Performance 2010 - 2019

2010 2011 2012 2013 2014 2015 2016 2017 2018 20190

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Performance 2010 - 2019

01260 08920 17110 25940 31030 46650

Example Output 5 – UK Energy Sector Concentration Ratio by Fuel Type

Output 1 - UK Energy Sector Concentration Ratios by Fuel Type (3 Largest By Turnover Companies)

Example Output 6 – Supermarket Locations

Notes for presenters

Example 1 – The upper table presents data for the UK, the lower table has a breakdown by country but Northern Ireland hasn’t been included. We can work out by subtracting the breakdown figures that for Osteosarcoma there were 5 cases in Northern Ireland. This number is likely to be below the threshold.

Example 2 – We have row and column totals. As such when we have only one suppressed value in a row or column, it we can work out the suppressed value

Example 3 – We have a sufficient number of observations for the mean income. The highest earner income is problematic as it is the maximum value, and may refer to a single observation. Some may feel that there is a group disclosure problem with the English as Main Language column. These figures could be considered safe because, in a majority English speaking country, we may expect these figures to be over 90%.

Example 4 – It would be impossible for an output checker to make an assessment of this graph, there isn’t any information presented with it to help with understanding. At a minimum we would want to know how many observations are represented by each line, and what the axes show.

Example 5 – A concentration shows what percentage of the total is accounted for by the largest observations, as stated in the caption for this output it is the three largest companies. Some of the fuel types have a high concentration ratio, for example the three largest companies producing Biofuels account for 85% of the production. This may indicate the presence of a dominant company and thereby breach the threshold rule. We would want to check that no company accounted for more than 43.75% of that sector.

Example 6 – This output is designed to stimulate discussion as it can be interpreted in two ways. On one side it could be argued that the precise geographic data is problematic as these refer to single observations. On the other, the supermarkets would not be likely to object to being identified in this way. It should not be accepted that SDC does not apply if the data is in the public domain; all data in a secure data environment is subject to the contracts agreed when the data was provided, and so the presence of information outside of a secure data environment does not mean that the same information can be released from a secure data environment. In such circumstances the researchers should be advised to use the data exists outside of the secure data environment and cite that as the source, so as not to imply the output checker had released something that could lead to identification.