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Table 3 Causes underlying positive and negative false cases in prostate cancer (with regard to the highest sensitive algorithm)

From: Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries

 

Granada Dataset

n (%)

Basque Country Dataset

n (%)

Murcia

Dataset

n (%)

Zaragoza

Dataset

n (%)

False negative cases

    

• Cases from private hospital in 2000

27 (14.5)

372 (42.7)

1 (0.3)

25 (9.3)

• Cases admitted in 2000 but discharged in 2001

3 (1.6)

98 (11.3)

41 (12.9)

43 (16.0)

• Cases coded in dx6 position or more

0 (0)

7 (0.8)

3 (0.9)

0 (0)

• False negative diagnoses

7 (3.8)

46 (5.3)

3 (0.9)

2 (0.7)

• Ambulatory care (instead of in-hospital care)

147 (79.0)

343 (39.3)

252 (79.7)

195(72.7)

• Loss using the algorithm

0 (0)

0 (0)

3(0.9)

0 (0)

• Other causes

2 (1.1)

4 (0.5)

13 (4.1)

3 (1.1)

 

186(100)

871(100)

316(100)

268(100)

False positive cases

    

• Cases registered in 2001

0 (0)

8 (2.3)

1 (0.8)

0 (0)

• Prevalent cases

56 (82.3)

280 (79.5)

74 (61.1)

122 (80.3)

• False positive diagnoses

7 (10.3)

15 (4.3)

29 (23.9)

25 (16.4)

• Missing in the registry

0 (0)

23 (6.5)

0 (0)

3 (1.9)

• Other residence than that covered by the registry

3 (4.4)

16 (4.5)

3 (2.5)

2 (1.3)

• Other causes

2 (2.9

10 (2.8)

14 (11.6)

0 (0)

 

68(100)

352(100)

121(100)

152(100)