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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)
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