Use of Antiepileptic Drugs and Risk of Prostate Cancer: A Nationwide Case-Control Study in Prostate Cancer Data Base Sweden
Table 1
Characteristics of prostate cancer cases and their matched controls in PCBaSe.
Number of patients
Cases (N = 31,591)
Controls (N = 156,802)
OR
95% CI
Characteristics
Age, n (%)
≤50
326
(1.0)
1640
(1.0)
1.00
(ref.)
51–60
3654
(11.6)
18088
(11.5)
1.26
(0.72–2.20)
61–70
12579
(39.8)
62090
(39.6)
1.25
(0.69–2.26)
71–80
10966
(34.7)
54775
(34.9)
0.98
(0.53–1.79)
81+
4066
(12.9)
20209
(12.9)
1.01
(0.53–1.93)
Antiepileptic drugs use, n (%)
No
29853
(94.5)
147128
(93.8)
1.00
(ref.)
Yes
1738
(5.5)
9674
(6.2)
0.89
(0.84–0.93)
Civil status, n (%)
Single
11351
(35.9)
63001
(40.2)
1.00
(ref.)
Married
20240
(64.1)
93801
(59.8)
1.20
(1.17–1.23)
Education level,n (%)
Low
9561
(30.3)
51353
(32.8)
1.00
(ref.)
Middle
13099
(41.5)
64304
(41.0)
1.10
(1.07–1.14)
High
8931
(28.3)
41145
(26.2)
1.18
(1.14–1.22)
CCI,n (%)
0
25999
(82.3)
126396
(80.6)
1.00
(ref.)
1
2982
(9.4)
15666
(10.0)
0.92
(0.88–0.96)
2
1410
(4.5)
7311
(4.7)
0.93
(0.88–0.99)
3+
1200
(3.8)
7429
(4.7)
0.78
(0.73–0.83)
Outpatient visits 1–10 years prior to inclusion, n (%)
No visits
12098
(38.3)
63160
(40.3)
1.00
(ref.)
1 visit
4662
(14.8)
21914
(14.0)
1.11
(1.07–1.16)
2 visits
3412
(10.8)
15965
(10.2)
1.12
(1.07–1.17)
3–5 visits
5615
(17.8)
25823
(16.5)
1.14
(1.10–1.18)
6–9 visits
2957
(9.4)
14063
(9.0)
1.10
(1.06–1.15)
10+ visits
2847
(9.0)
15877
(10.1)
0.94
(0.90–0.98)
Cumulative length of hospital stay 1–10 years prior to inclusion, n (%)
No visits
19378
(61.3)
91725
(58.5)
1.00
(ref.)
1–3 days
4145
(13.1)
20111
(12.8)
0.97
(0.93–1.00)
4–7 days
3123
(9.9)
15233
(9.7)
0.96
(0.92–1.00)
1-2 weeks
2298
(7.3)
11970
(7.6)
0.89
(0.85–0.94)
3-4 weeks
1541
(4.9)
9159
(5.8)
0.78
(0.73–0.82)
4+ weeks
1106
(3.5)
8604
(5.5)
0.59
(0.56–0.63)
SD: standard deviation, education: low (elementary school), middle (9–12 years in education/gymnasium), and high (>12 years in education/university), and CCI: Charlson comorbidity index. Deviations from 1 are seen in the ORs because there is not a complete correspondence in age groups between cases and controls, as this would lead to a singular design matrix in the logistic regression model. Despite matching, ORs deviating from 1 with notable imprecision are seen due to controls being matched to cases based on year of birth, while age was calculated based on year and quarter of birth.
