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Table 2 Distribution of unique study identification numbers between cancer registry and hospital separations.

From: In the absence of cancer registry data, is it sensible to assess incidence using hospital separation records?

Study identification numbers

Cancer registry present

Cancer registry absent

Totals

Hospital separations present

a

b

(a + b)

Hospital separations absent

c

d

(c + d)

Total

(a + c)

(b + d)

(a + b + c + d)

  1. Notes.
  2. 1. Total unique study identification numbers = (a + b + c)
  3. 2. All study identification numbers from BCCA cancer incidence registry = (a + c)
  4. 3. All study identification numbers from hospital cancer separations = (a + b)
  5. 4. Shared study identification numbers between BCCA registry and hospital separations = a
  6. 5. Study identification numbers only in the BCCA registry = b
  7. 6. Study identification numbers only in hospital separations = c
  8. 7. Reference population used for annual calculations is the mid-year VIHA population = (a + b + c + d)
  9. 8. Count "d" represents the balance of the VIHA mid-year population appearing in neither registry nor separations records
  10. 9. In the case of the 1990–1999 aggregate file, the ten-year mean mid-year population is designated as the reference population; see discussion in text for effect of this assumption
  11. 10. The cancer registry is taken as the gold standard
  12. Measures.
  13. 1. Similarity = [(a + b)/(a + c)]
  14. 2. Sensitivity = [a/(a + c)]
  15. 3. Specificity = [d/(b + d)]
  16. 4. Positive predictive value = [a/(a + b)]
  17. 5. Negative predictive value = [d/(c + d)]
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International Journal for Equity in Health

ISSN: 1475-9276

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