The association of menopausal hormone therapy with the incidence of urinary tract cancer: a national population-based study

The association of menopausal hormone therapy with the incidence of urinary tract cancer: a national population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The association of menopausal hormone therapy with the incidence of urinary tract cancer: a national population-based study Jin-Sung Yuk, Sang-Hee Yoon, Ji Hyeong Yu, Jae Yoon Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2148280/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study evaluates the relationship between menopausal hormone therapy (MHT) and the risk of urologic cancer in women. It was conducted for South Korea’s national population based on the National Health Insurance Service Database between January 2002 and January 2019. The types of MHT in this study included tibolone, combined oestrogen plus progestin by the manufacturer (CEPM) or physician (CEPP), and oral and topical oestrogen. Furthermore, select patient characteristics and reproductive factors were reviewed. We performed a Cox proportional hazard analysis to clarify the risk of urologic cancer associated with MHT. According to MHT types, 104,089 were treated with tibolone, 65,597 with CEPM, 29,357 with oral oestrogen, 3,913 with CEPP, and 1,174 with topical oestrogen. Among women on MHT, the incidence of kidney cancer was significantly increased with oral oestrogen (hazard ratio [HR] 1.36, 95% confidence interval [CI]: 1.062–1.735) and topical oestrogen (HR 2.84, 95% CI: 1.270–6.344), whereas other formulations were not associated with kidney cancer. Meanwhile, tibolone significantly decreased the incidence of bladder cancer (HR 0.69, 95% CI: 0.548–0.858), whereas other formulations were not associated with bladder cancer. Our findings suggest that MHT in postmenopausal women affects the incidence of urologic cancers. menopausal hormone therapy urological cancer oestrogen kidney cancer bladder cancer Figures Figure 1 Figure 2 Introduction Many women experience various symptoms of menopause, including vasomotor symptoms (sweating and hot flushes), vaginal symptoms (sexual discomfort and vaginal dryness), psychological problems (anxiety and low mood), frequent urinary infections, and urinary incontinence [ 1 ], which are caused by the natural decline in oestrogen and progesterone levels. Hence, menopausal hormone therapy (MHT) has been widely prescribed to alleviate symptoms. MHT comes in several formulations, the most common of which are oestrogen, combined oestrogen and progestogen, and tibolone, a synthetic steroid. Although there are benefits to MHT use [ 2 ], several studies report that MHT can increase the risk of stroke, cardiovascular disease, cholecystitis, meningioma, breast cancer, and ovarian cancer [ 3 , 4 ]. Previous reports reveal that MHT use is particularly associated with urologic cancer in women, including kidney [ 5 – 8 ] and bladder cancer [ 9 – 12 ]. Kidney cancer is the 16th most common cancer worldwide, accounting for 2.2% of all newly diagnosed cases of malignancy [ 13 ]. Risk factors for kidney cancer include cigarette smoking, hypertension, obesity, and chronic kidney disease [ 14 ]; however, the aetiology of the disease remains unknown. Bladder cancer is the ninth most frequently diagnosed cancer worldwide. In 2018, the estimated number of newly diagnosed cases of bladder cancer in men and women in the United States was 62,100 and 19,300, respectively [ 13 ]. The most common risk factors for bladder cancer are smoking and occupational exposure to aromatic amines [ 15 ]. Interestingly, epidemiological studies have shown that the incidence of both types of urologic cancer is three- to four-fold times higher in men than in women [ 13 ], which may be because more men smoke; however, the known risk factors are unlikely to explain the differences in the incidence of urologic cancer between men and women. Another possible factor is the difference in the levels of hormones between sexes, including hormonal changes after menopause and, particularly, the use of exogenous hormones for MHT. Previous research reports on the association between hormonal factors, including exogenous hormone use for MHT, and the incidence of urologic cancer in women, including kidney and bladder cancer; however, the results are inconsistent. Therefore, this study aimed to evaluate the relationship between MHT and urologic cancer risk in women using data from the Korean Health Insurance Review and Assessment Service (HIRA). Results Characteristics of women on MHT Among the 2,506,271 women who underwent national health examinations between 2002 and 2011 and after excluding those with urinary tract diseases or cancer, 557,031 and 204,130 were classified into the non-MHT and MHT groups, respectively (Fig. 1 ). The mean age and BMI were 56 (52–62) years and 23.8 (21.9–25.9) kg/m 2 , respectively. The follow-up periods for the non-MHT and MHT groups were 12.2 years and 13.4 years, respectively. Among the MHT group, 104,089 women were on tibolone, 65,597 on CEPM, 29,357 on oral oestrogen alone, 3,913 on CEPP, and 1,174 on topical oestrogen. Table 1 shows the detailed characteristics of all participants in this study. Table 1 Characteristics of women according to menopausal hormone exposure status at recruitment, Korea National Health Insurance Data, 2002–2019 Non-MHT Tibolone Combined oestrogen plus progestin by manufacturer Oral oestrogen Combined oestrogen plus progestin by physician Topical oestrogen Total Number of women 557,031 104,089 65,597 29,357 3,913 1,174 761,161 Median age (years) 58 [52–64] 54 [51–58] 53 [50–56] 53 [49–57] 55 [51–59] 53 [50–57] 56 [52–62] Age at inclusion (years) 40 ~ 49 52,832 (9.5) 17,704 (17) 14,788 (22.5) 7,667 (26.1) 665 (17) 247 (21) 93,903 (12.3) 50 ~ 59 269,365 (48.4) 67,612 (65) 43,189 (65.8) 16,524 (56.3) 2,274 (58.1) 720 (61.3) 399,684 (52.5) 60 ~ 69 168,878 (34.4) 16,789 (16.4) 7,070 (10.9) 4,372 (15.3) 866 (22.8) 192 (16.6) 198,167 (28.6) 70~ 65,956 (11.8) 1,984 (1.9) 550 (0.8) 794 (2.7) 108 (2.8) 15 (1.3) 69,407 (9.1) Median BMI (kg/m 2 ) 24 [22.1–26.1] 23.5 [21.8–25.4] 23.1 [21.5–25] 23.7 [22-25.7] 23.3 [21.6–25.2] 23.7 [22-25.6] 23.8 [21.9–25.9] BMI (kg/m 2 ) < 18.5 10,547 (1.9) 1,710 (1.7) 1,330 (2) 428 (1.5) 72 (1.9) 21 (1.8) 14,108 (1.9) 18.5–22.9 188,340 (34.5) 40,595 (39.5) 28,798 (44.2) 10,538 (36.2) 1,583 (40.8) 436 (37.5) 270,290 (36.2) 23-24.9 144,971 (26.6) 28,732 (27.9) 17,594 (27) 8,255 (28.4) 1,097 (28.3) 303 (26.1) 200,952 (26.9) 25-29.9 178,877 (32.8) 29,093 (28.3) 16,014 (24.6) 8,887 (30.6) 1,039 (26.8) 372 (32) 234,282 (31.3) ≥ 30 22,810 (4.2) 2,761 (2.7) 1,348 (2.1) 981 (3.4) 87 (2.2) 30 (2.6) 28,017 (3.7) SES Mid ~ high SES 532,414 (95.6) 100,037 (96.1) 63,850 (97.3) 28,504 (97.1) 3,821 (97.6) 1,138 (96.9) 729,764 (95.9) Low SES 24,617 (4.4) 4,052 (3.9) 1,747 (2.7) 853 (2.9) 92 (2.4) 36 (3.1) 31,397 (4.1) Region Urban area 164,762 (29.6) 33,685 (32.4) 23,255 (35.5) 9,778 (33.3) 2,066 (52.8) 561 (47.8) 234,107 (30.8) Rural area 392,269 (70.4) 70,404 (67.6) 42,342 (64.5) 19,579 (66.7) 1,847 (47.2) 613 (52.2) 527,054 (69.2) CCI 0 375,267 (67.4) 73,114 (70.2) 48,200 (73.5) 21,107 (71.9) 2,798 (71.5) 795 (67.7) 521,281 (68.5) 1 102,894 (18.5) 19,370 (18.6) 11,097 (16.9) 5,105 (17.4) 698 (17.8) 199 (17) 139,363 (18.3) ≥ 2 78,870 (14.2) 11,605 (11.1) 6,300 (9.6) 3,145 (10.7) 417 (10.7) 180 (15.3) 100,517 (13.2) Parity (years) 0 or no response 114,181 (20.5) 19,045 (18.3) 10,105 (15.4) 6,873 (23.4) 862 (22) 264 (22.5) 151,330 (19.9) 1 31,275 (5.6) 8,689 (8.3) 6,505 (9.9) 2,135 (7.3) 267 (6.8) 92 (7.8) 48,963 (6.4) 2 330,424 (69.4) 65,627 (70.3) 43,480 (72.4) 17,184 (65.6) 2,333 (67.4) 676 (65.5) 459,724 (69.7) ≥ 3 81,151 (14.6) 10,728 (10.3) 5,507 (8.4) 3,165 (10.8) 451 (11.5) 142 (12.1) 101,144 (13.3) Age at menarche (years) < 13 104,413 (18.9) 16,245 (15.8) 10,047 (15.4) 5,574 (19.3) 745 (19.2) 237 (20.5) 137,261 (18.2) ≥ 13 448,932 (81.1) 86,894 (84.2) 55,103 (84.6) 23,373 (80.7) 3,137 (80.8) 919 (79.5) 618,358 (81.8) Age at menopause (years) 40–44 76,978 (13.8) 13,353 (12.8) 8,086 (12.3) 6,551 (22.3) 523 (13.4) 233 (19.8) 105,724 (13.9) 45–49 161,668 (29) 34,335 (33) 22,038 (33.6) 10,446 (35.6) 1,261 (32.2) 444 (37.8) 230,192 (30.2) 50–54 271,690 (53.2) 48,373 (50.4) 30,939 (50.7) 10,969 (39.2) 1,799 (50.2) 421 (38.3) 364,191 (52) 55- 46,695 (8.4) 8,028 (7.7) 4,534 (6.9) 1,391 (4.7) 330 (8.4) 76 (6.5) 61,054 (8) Smoking Never 507,290 (96.3) 93,508 (93.7) 59,150 (93.4) 26,744 (95) 3,606 (96.1) 1,063 (95.9) 691,361 (95.7) Past 5,299 (1) 1,647 (1.7) 1,122 (1.8) 379 (1.3) 41 (1.1) 19 (1.7) 8,507 (1.2) Current 13,984 (2.7) 4,610 (4.6) 3,048 (4.8) 1,037 (3.7) 106 (2.8) 26 (2.3) 22,811 (3.2) Alcohol (per week) None 450,396 (85.2) 78,540 (78.1) 48,955 (76.8) 22,758 (80.1) 3,148 (83.1) 930 (82.6) 604,727 (83.2) ~ 2/week 67,537 (12.8) 18,817 (18.7) 12,708 (19.9) 4,958 (17.4) 580 (15.3) 173 (15.4) 104,773 (14.4) 3 ~ 6/week 7,907 (1.5) 2,408 (2.4) 1,654 (2.6) 516 (1.8) 42 (1.1) 19 (1.7) 12,546 (1.7) Daily 3,037 (0.6) 798 (0.8) 413 (0.6) 189 (0.7) 17 (0.4) 4 (0.4) 4,458 (0.6) Physical exercise (per week) None 341,799 (64.5) 58,669 (58.4) 37,461 (58.8) 16,584 (58.4) 2,101 (55.6) 597 (53.1) 457,211 (62.9) 1 ~ 2 88,768 (16.8) 19,363 (19.3) 12,641 (19.8) 5,571 (19.6) 743 (19.7) 228 (20.3) 127,314 (17.5) 3 ~ 4 49,298 (9.3) 11,769 (11.7) 7,503 (11.8) 3,202 (11.3) 511 (13.5) 168 (14.9) 72,451 (10) 5 ~ 6 16,335 (3.1) 3,866 (3.8) 2,532 (4) 1,001 (3.5) 148 (3.9) 51 (4.5) 23,933 (3.3) Daily 33,461 (6.3) 6,824 (6.8) 3,583 (5.6) 2,029 (7.1) 274 (7.3) 81 (7.2) 46,252 (6.4) Period from menopause to inclusion (years) < 5 214,939 (38.6) 57,705 (55.4) 42,930 (65.4) 15,163 (51.7) 1,931 (49.3) 592 (50.4) 333,260 (43.8) 5 ~ 9 122,455 (22) 25,240 (24.2) 13,963 (21.3) 7,584 (25.8) 965 (24.7) 316 (26.9) 170,523 (22.4) 10~ 219,637 (39.4) 21,144 (20.3) 8,704 (13.3) 6,610 (22.5) 1,017 (26) 266 (22.7) 257,378 (33.8) BMI: Body mass index, CCI: Charlson comorbidity index, MHT: menopausal hormone therapy, SES: socioeconomic status Data are expressed as the number (%) or median [25th percentile, 75th percentile]. Incidence Of Urologic Cancer In Women On Various Formulations Of Mht Kidney cancer occurred in 997 (0.2%) women in the non-MHT group. In the MHT group, kidney cancer occurred in 190 (0.2%), 106 (0.2%), 75 (0.3%), 4 (0.1%), and 7 (0.6%) women treated with tibolone, CEPM, oral oestrogen alone, CEPP, and topical oestrogen, respectively. Among the women in the non-MHT group, bladder cancer occurred in 910 (0.2%) patients. In the MHT group, bladder cancer occurred in 104 (0.1%), 60 (0.1%), 35 (0.1%), 5 (0.1%), and 5 (0.4%) women treated with tibolone, CEPM, oral oestrogen alone, CEPP, and topical oestrogen, respectively. The median duration of hormone therapy in the MHT group was 23 (10–55) months. Additionally, 77.2% of women in the MHT group received hormone therapy within 5 years after menopause (Table 2 ). Table 2 Characteristics of women on menopausal hormones, Korea National Health Insurance Data, 2002–2019 MHT characteristics Tibolone Combined oestrogen plus progestin by manufacturer Oral oestrogen Combined oestrogen plus progestin by physician Topical oestrogen Total MHT Median duration (months) 25 [11–58] 24 [11–57] 15 [ 9 – 39 ] 16 [ 9 – 34 ] 14 [ 9 – 26 ] 23 [10–55] Duration (years) < 5 78,686 (75.6) 50,015 (76.2) 24,397 (83.1) 3,413 (87.2) 1,114 (94.9) 157,625 (77.2) 5-9.9 17,807 (17.1) 11,502 (17.5) 3,273 (11.1) 387 (9.9) 57 (4.9) 33,026 (16.2) ≥ 10 7,596 (7.3) 4,080 (6.2) 1,687 (5.7) 113 (2.9) 3 (0.3) 13,479 (6.6) Duration of previous other MHT (years) < 5 101,557 (97.6) 64,660 (98.6) 28,987 (98.7) 3,287 (84) 1,163 (99.1) 199,654 (97.8) 5-9.9 2,244 (2.2) 858 (1.3) 324 (1.1) 466 (11.9) 11 (0.9) 3,903 (1.9) ≥ 10 288 (0.3) 79 (0.1) 46 (0.2) 160 (4.1) 0 (0) 573 (0.3) Last dosage of Tibolone (per day) 1.25 mg 983 (0.9) 2.5 mg 103,002 (99) over 5 mg 97 (0.1) Prescribed specialty Gynaecology 33,073 (31.8) 29,160 (44.5) 11,120 (37.9) 908 (23.2) 263 (22.4) 74,524 (36.5) Non-gynaecology 71,016 (68.2) 36,437 (55.5) 18,237 (62.1) 3,005 (76.8) 911 (77.6) 129,606 (63.5) MHT: menopausal hormone therapy Data are expressed as the number (%) or median [25th percentile, 75th percentile]. In the Cox proportional hazard analysis that adjusted for variables such as age, the incidence of kidney cancer was significantly increased in women treated with oral oestrogen alone (hazard ratio [HR] 1.36, 95% confidence interval [CI]: 1.062–1.735) and topical oestrogen (HR 2.84, 95% CI: 1.270–6.344) (Fig. 2 ). However, the incidences of kidney cancer among women treated with tibolone (HR 0.99, 95% CI: 0.846–1.180), CEPM (HR 1.02, 95% CI: 0.830–1.264), and CEPP (HR 0.55, 95% CI: 0.204–1.456) were not different (Table 3 ). Table 3 Hazard ratios for risk of urogenital cancer according to MHT drug type, Korea National Health Insurance Data, 2002–2019 MHT HR (95% CI) P-value Renal cancer Tibolone 0.999 (0.846–1.18) 0.992 Combined oestrogen plus progestin by manufacturer 1.024 (0.83–1.264) 0.822 Oestrogen 1.358 (1.062–1.735) 0.015 Combined oestrogen plus progestin by physician 0.545 (0.204–1.456) 0.226 Topical oestrogen 2.839 (1.27–6.344) 0.011 Malignancy of the renal pelvis Tibolone 0.735 (0.432–1.25) 0.256 Combined oestrogen plus progestin by manufacturer 1.131 (0.628–2.036) 0.681 Oestrogen 2.027 (1.133–3.626) 0.017 Combined oestrogen plus progestin by physician 0 (0-5.28E + 207) 0.965 Topical oestrogen 0 (0-.) 0.982 Malignancy of the ureter Tibolone 0.769 (0.51–1.16) 0.21 Combined oestrogen plus progestin by manufacturer 0.946 (0.57–1.571) 0.831 Oestrogen 1.225 (0.695–2.161) 0.483 Combined oestrogen plus progestin by physician 0.625 (0.087–4.468) 0.639 Topical oestrogen 0 (0-1.26E + 194) 0.966 Bladder cancer Tibolone 0.686 (0.548–0.858) 0.001 Combined oestrogen plus progestin by manufacturer 0.809 (0.613–1.069) 0.136 Oestrogen 0.856 (0.602–1.218) 0.388 Combined oestrogen plus progestin by physician 0.832 (0.345–2.007) 0.682 Topical oestrogen 2.591 (0.968–6.934) 0.058 CI: confidence interval, HR: hazard ratio, MHT: menopausal hormone therapy HRs were adjusted for age group, body mass index, socioeconomic status, region, Charlson comorbidity index, parity, age at menarche, age at menopause, smoking, alcohol consumption, physical exercise, and period from menopause to inclusion. Notably, tibolone significantly decreased the incidence of bladder cancer (HR 0.69, 95% CI: 0.548–0.858) (Fig. 2 ), whereas other formulations did not affect the incidence of bladder cancer (Table 3 ). In the dose-dependent analysis, tibolone had no preventive effect on bladder cancer when 1.25 mg (half dose) was given (HR 1.43, 95% CI: 0.357–5.735) (Table 2 ). Association Of Risk Factors With Urologic Cancer Development In Women On Mht In the analysis that adjusted for variables, the incidence of kidney cancer increased with older age (≥ 70 years; HR 2.25, 95% CI: 1.54–3.293), obesity (≥ 30 kg/m 2 ; HR 1.81, 95% CI: 1.388– 2.367), current smoking status (HR 1.37, 95% CI: 1.014–1.851), and exercise (5–6 times/week; HR 1.38, 95% CI: 1.042–1.822) (Table 4 ). In contrast, living in a rural area (HR 0.88, 95% CI: 0.782–0.99) and drinking alcohol (2 drinks/week; HR 0.77, 95% CI: 0.642–0.918) decreased the risk of kidney cancer. Additionally, the reproductive factors of age at menarche and at menopause, as well as parity, were not associated with the incidence of kidney cancer. Table 4 Hazard ratios for risk of urogenital cancer according to major variables, Korea National Health Insurance Data, 2002–2019 Renal cancer a Malignancy of the renal pelvis a Malignancy of the ureter a Bladder cancer a Variables HR (95% CI) P-value HR (95% CI) a P-value HR (95% CI) a P-value HR (95% CI) a P-value MHT Tibolone 0.999 (0.846–1.18) 0.992 0.735 (0.432–1.25) 0.256 0.769 (0.51–1.16) 0.21 0.686 (0.548–0.858) 0.001 Combined oestrogen plus progestin by manufacturer 1.024 (0.83–1.264) 0.822 1.131 (0.628–2.036) 0.681 0.946 (0.57–1.571) 0.831 0.809 (0.613–1.069) 0.136 Oral oestrogen 1.358 (1.062–1.735) 0.015 2.027 (1.133–3.626) 0.017 1.225 (0.695–2.161) 0.483 0.856 (0.602–1.218) 0.388 Combined oestrogen plus progestin by physician 0.545 (0.204–1.456) 0.226 0 (0-5.28E + 207) 0.965 0.625 (0.087–4.468) 0.639 0.832 (0.345–2.007) 0.682 Topical oestrogen 2.839 (1.27–6.344) 0.011 0 (0-.) 0.982 0 (0-1.26E + 194) 0.966 2.591 (0.968–6.934) 0.058 Age at inclusion (years) 50 ~ 59 1.449 (1.148–1.83) 0.002 1.527 (0.71–3.286) 0.279 2.317 (1.182–4.543) 0.014 1.787 (1.277-2.5) < 0.001 60 ~ 69 1.732 (1.249–2.402) 0.001 2.094 (0.788–5.566) 0.138 3.888 (1.719–8.794) 0.001 3.141 (2.064–4.781) < 0.001 70~ 2.252 (1.54–3.293) < 0.001 4.086 (1.406–11.87) 0.01 7.765 (3.221–18.72) < 0.001 5.374 (3.408–8.475) < 0.001 BMI (kg/m 2 ) < 18.5 0.695 (0.39–1.236) 0.215 1.125 (0.35–3.623) 0.843 0.171 (0.024–1.226) 0.079 1.137 (0.737–1.752) 0.562 23-24.9 1.379 (1.188–1.601) < 0.001 1.489 (1.011–2.194) 0.044 0.875 (0.643–1.19) 0.395 1.007 (0.856–1.184) 0.936 25-29.9 1.624 (1.413–1.868) < 0.001 1.414 (0.972–2.057) 0.07 1.052 (0.798–1.388) 0.717 1.017 (0.872–1.185) 0.83 ≥ 30 1.813 (1.388–2.367) < 0.001 0.704 (0.253–1.958) 0.501 1.082 (0.606–1.934) 0.79 1.232 (0.912–1.666) 0.174 SES Low SES 1.118 (0.797–1.57) 0.518 0.841 (0.31–2.282) 0.733 1.004 (0.493–2.043) 0.991 1.072 (0.742–1.547) 0.712 Region Rural area 0.88 (0.782–0.99) 0.033 1.057 (0.759–1.471) 0.744 1.032 (0.796–1.338) 0.813 0.81 (0.709–0.926) 0.002 CCI 1 1.062 (0.922–1.223) 0.406 1.197 (0.833–1.721) 0.33 1.142 (0.858–1.519) 0.364 1.275 (1.097–1.482) 0.002 ≥ 2 0.986 (0.835–1.165) 0.87 1.134 (0.739–1.739) 0.566 1.035 (0.734–1.458) 0.846 1.109 (0.924–1.33) 0.266 Parity (years) 0 1.283 (0.938–1.756) 0.119 2.013 (0.763–5.31) 0.157 0.936 (0.522–1.677) 0.823 0.858 (0.628–1.171) 0.333 2 1.297 (0.982–1.714) 0.067 1.544 (0.625–3.814) 0.347 0.66 (0.391–1.115) 0.121 0.705 (0.538–0.925) 0.012 ≥ 3 1.175 (0.865–1.597) 0.302 1.516 (0.582–3.951) 0.395 0.68 (0.382–1.212) 0.191 0.804 (0.597–1.082) 0.15 Age at menarche (years) ≥ 13 0.964 (0.804–1.156) 0.692 0.973 (0.602–1.572) 0.91 1.161 (0.809–1.667) 0.418 0.966 (0.785–1.188) 0.742 Age at menopause (years) 45–49 0.913 (0.752–1.108) 0.358 1.47 (0.826–2.618) 0.19 0.837 (0.559–1.252) 0.386 1.166 (0.931–1.46) 0.181 50–54 0.966 (0.782–1.193) 0.746 1.874 (1.025–3.424) 0.041 1.155 (0.767–1.739) 0.49 1.262 (0.998–1.597) 0.052 55- 0.919 (0.679–1.245) 0.586 1.828 (0.798–4.19) 0.154 1.228 (0.675–2.233) 0.501 1.068 (0.756–1.508) 0.711 Smoking Past 0.687 (0.356–1.326) 0.264 0.599 (0.084–4.298) 0.611 0.363 (0.051–2.592) 0.312 1.125 (0.619–2.043) 0.7 Current 1.37 (1.014–1.851) 0.04 1.139 (0.462–2.809) 0.777 1.817 (1.029–3.21) 0.04 1.957 (1.463–2.619) < 0.001 Alcohol (g/week) ~ 2/week 0.768 (0.642–0.918) 0.004 0.749 (0.444–1.263) 0.278 0.798 (0.533–1.196) 0.275 0.924 (0.754–1.131) 0.442 3 ~ 6/week 1.136 (0.748–1.726) 0.549 0.894 (0.22–3.641) 0.876 1.118 (0.412–3.034) 0.826 0.437 (0.195–0.98) 0.044 Daily 0.841 (0.399–1.772) 0.649 1.869 (0.461–7.586) 0.382 1.088 (0.269–4.398) 0.905 1.681 (0.924–3.058) 0.089 Physical exercise (per week) 1 ~ 2 1.165 (1.007–1.349) 0.041 0.821 (0.527–1.28) 0.384 1.004 (0.724–1.392) 0.98 0.97 (0.813–1.159) 0.739 3 ~ 4 1.069 (0.885–1.292) 0.486 1.385 (0.881–2.177) 0.159 1.228 (0.84–1.794) 0.289 1.092 (0.881–1.352) 0.422 5 ~ 6 1.378 (1.042–1.822) 0.025 0.932 (0.38–2.288) 0.878 0.596 (0.245–1.451) 0.254 1.263 (0.906–1.762) 0.169 Daily 1.158 (0.94–1.427) 0.169 0.768 (0.413–1.429) 0.405 0.9 (0.572–1.416) 0.649 0.983 (0.774–1.249) 0.89 Period from menopause to inclusion (years) 5 ~ 9 1.064 (0.904–1.254) 0.454 1.347 (0.832–2.182) 0.226 1.45 (0.974–2.159) 0.067 1.219 (0.986–1.506) 0.067 10~ 1.028 (0.814–1.298) 0.818 1.603 (0.844–3.041) 0.149 1.72 (1.039–2.85) 0.035 1.504 (1.146–1.975) 0.003 BMI: Body mass index, CCI: Charlson comorbidity index, CI: confidence interval, HR: hazard ratio, MHT: menopausal hormone therapy, SES: socioeconomic status a HRs were adjusted for age group, body mass index, socioeconomic status, region, Charlson comorbidity index, parity, age at menarche, age at menopause, smoking, alcohol consumption, physical exercise, and period from menopause to inclusion. The adjusted Cox proportional hazard analysis revealed that the incidence of bladder cancer was higher in older women (≥ 70 years; HR 5.37, 95% CI: 3.408–8.475), current smokers (HR 1.96, 95% CI: 1.463–2.619), and those included in the study after > 10 years from menopause (HR 1.50, 95% CI: 1.146–1.975) (Table 4 ). In contrast, living in rural areas (HR 0.81, 95% CI: 0.709–0.926), parity (parity, 2; HR 0.71, 95% CI: 0.538–0.925), and drinking alcohol (3–6 drinks/week; HR 0.44, 95% CI: 0.195–0.980) decreased the incidence of bladder cancer. BMI, low SES, age at menarche and at menopause, and physical exercise were not associated with the incidence of bladder cancer. Incidence Of Urologic Cancer For Various Mht Formulations According To Age In the age-group analysis, oral oestrogen increased the risk of kidney cancer only in women aged 50–59 years old (HR 1.38, 95% CI: 1.008–1.883), while topical oestrogen increased the risk of kidney cancer only in women aged 60–69 years old (HR 4.83, 95% CI: 1.198–19.461). Tibolone significantly decreased the incidence of bladder cancer only in women aged 50–59 years old (HR 0.69, 95% CI: 0.508–0.934), whereas it only had a tendency to lower the incidence of bladder cancer in women aged 60–69 years old (HR 0.69, 95% CI: 0.467–1.016). Discussion Our study reveals that the use and type of MHT might affect the incidence of urologic cancer among postmenopausal women; oral and topical oestrogen formulations significantly increased the incidence of kidney cancer in women on MHT. However, other types of MHT were not significantly associated with the incidence of kidney cancer. Previous studies reveal that the risk of kidney cancer in postmenopausal women differs depending on the type of MHT. Among the types of MHT, studies that describe the association of oestrogen alone with kidney cancer show conflicting results [ 5 – 7 ]. Molokwu et al. [ 7 ] discovered that the risk of kidney cancer was significantly increased in women using oestrogen alone. Similarly, McCredie et al. [ 8 ] reveal that women using oestrogen alone had a significantly increased risk of kidney cancer. Additionally, Zhang et al. [ 16 ] performed a meta-analysis of previously reported case–control and cohort studies and demonstrated that oestrogen alone was associated with an increased risk of kidney cancer, which is consistent with our findings. Two other studies report on the relationship between the risk of kidney cancer and combined oestrogen plus progestin; however, the relationships were nonsignificant [ 5 , 6 ]. Setiawan et al. demonstrate that among 106,036 women, those on combined oestrogen plus progestin therapy had a nonsignificant 27% increase in the risk of kidney cancer [ 5 ]. Meanwhile, among a population of 118,219 United States nurses, another study reports that women using combined oestrogen plus progestin had a nonsignificant decrease in the risk of kidney cancer [ 6 ]. The underlying mechanisms regarding oestrogen therapy affecting the risk of kidney cancer remain unclear; however, several studies have proposed potential mechanisms. Tanaka et al. discovered that oestrogen and progesterone receptors in women are expressed in both normal and cancerous kidney tissue [ 17 ], which is also reported by other studies that suggest that adjusting endocrine functions can partially reduce the risk of kidney cancer. Moreover, several experimental studies have suggested that the estrogenic effect is related to the occurrence of renal cell tumours in animals [ 18 ]. Oestrogen alone is only prescribed for patients who have undergone a hysterectomy. The most common indications for a hysterectomy are uterine fibroids and adenomyosis. Therefore, there might be an association between uterine fibroids, adenomyosis, hysterectomies, and kidney cancer. Zucchetto et al. [ 19 ] also report on an increased incidence of kidney cancer in patients who underwent a hysterectomy. In the analysis that adjusted for variables, the incidence of kidney cancer was associated with older age, obesity, and current smoking status, which are consistent with previously known factors [ 14 ]. Contrastingly, the incidence of kidney cancer was lower in women living in rural areas. Obesity is associated with an increased risk of kidney cancer [ 14 , 20 ]. A previous study reports that the proportion of obese adults is higher in rural areas than in urban areas [ 21 ]. Additionally, previous studies demonstrate that smoking is a significant risk factor for kidney cancer [ 14 , 22 ], with men and women who smoke having a 1.54 and 1.22 higher risk of the disease, respectively, than non-smokers [ 22 ]. Sadowski et al. [ 23 ] discovered that residents in rural areas have a lower incidence of kidney cancer but a higher mortality from the disease than those in urban areas, which is consistent with our findings and may be attributed to a disparity in available diagnostic utilization and hospital access between rural and urban areas. One study has reported that many hospitals in rural areas lacked qualified neuroimaging modalities [ 24 ]. In our study, reproductive factors, such as parity, as well as age at menarche and at menopause, were not associated with the incidence of kidney cancer. Tibolone significantly decreased the incidence of bladder cancer, whereas other MHT formulations were not significantly associated with the incidence of bladder cancer. Consistent with previous studies is that CEPM did not significantly decrease the incidence of bladder cancer [ 9 , 12 ]. Research has suggested that MHT in postmenopausal women affects the risk of bladder cancer, but the results have been inconsistent. Several studies have uncovered no significant association between MHT and bladder cancer [ 25 – 27 ]; however, a few cohort studies have reported that the risk of bladder cancer may vary depending on the MHT formulation [ 9 – 12 ]. Studies have uncovered that oestrogen alone was not significantly associated with an increased risk of bladder cancer; however, there may still be an association, albeit nonsignificant [ 9 – 11 ]. Daugherty et al. [ 12 ] reported on an inverse association between combined oestrogen plus progestin and bladder cancer, and Davis-Dao et al. [ 9 ] discovered similar results. However, a meta-analysis by Xu et al. [ 28 ] reveals that combined oestrogen plus progestin was negatively but not significantly associated with bladder cancer. Additionally, McGrath et al. [ 11 ] report that women on combined oestrogen plus progestin therapy did not have a significantly lower risk of bladder cancer compared with women not using combined oestrogen plus progestin. However, a 2020 study with longer observation periods and more participants conducted by the same researcher involved no inverse association between combined oestrogen plus progestin and the risk of bladder cancer. The exact mechanisms underlying the results of previous observational studies are unknown; however, previous experimental research has uncovered that sex hormonal signalling plays an important role in the incidence and progression of bladder cancer [ 29 – 32 ]. An underlying mechanism could be that sex hormones have important physiological effects in maintaining the structures and functions of the female lower urinary tract [ 33 – 35 ]. The female genital and lower urinary tracts have a common embryonic origin arising from the urogenital sinus; therefore, significant alterations of sex hormone levels, such as those occurring with pregnancy or menopause, can cause significant modifications in the lower urinary tract, causing various symptoms, including urgency, incontinence, and recurrent urinary tract infections [ 34 , 35 ]. Moreover, frequent and recurrent urinary tract infections may be followed by chronic irritation of the bladder epithelium, resulting in a significantly increased risk of bladder cancer [ 36 , 37 ]. Yu et al. demonstrate that sex hormones are essential in maintaining the bladder’s structure and that both oestrogen and androgen can reverse bladder muscle atrophy caused by ovariectomy [ 38 ]. Similarly, Xin et al. suggest that the deprivation of sex hormones negatively affects the bladder structure and histology, and that oestrogen administration can preserve bladder function by inhibiting collagen hyperplasia and increasing smooth muscle density [ 39 ]. In our study, tibolone significantly decreased the incidence of bladder cancer. There have been few studies on the association between tibolone use in postmenopausal women and the risk of bladder cancer. Tibolone has estrogenic, progestogenic, and androgenic effects with differential metabolism in each tissue, making it unique for treating menopausal symptoms [ 40 ]. The estrogenic effects of tibolone are primarily expressed in the vagina, bone tissue, and brain, with less expression in the endometrium. Considering the results of previous research, selective tissue-specific effects of various sex hormones through tibolone are thought to maintain the bladder’s structure and function, which may prevent urinary tract symptoms and infections and thus, significantly lower the risk of bladder cancer, which is consistent with our findings. In our study, older age and current smoking status significantly increased the incidence of bladder cancer, which is consistent with previously known factors [ 15 ]. In contrast, living in rural areas and parity significantly decreased the incidence of bladder cancer. Previous studies have shown that smoking is the most important risk factor for developing bladder cancer, with a 50% increased risk in smokers [ 41 ]. Similar to kidney cancer, several studies have uncovered that women in rural areas have a lower incidence of bladder cancer than those in urban areas [ 42 , 43 ], which is consistent with our results. Previous studies on the relationship between reproductive factors and the risk of bladder cancer did not show consistent results; however, several studies report that parity was negatively associated with the risk of bladder cancer [ 9 , 10 , 12 , 26 ]. Pregnancy causes dramatic changes in oestrogen and progesterone levels, which may persist for months. However, the exact mechanisms by which oestrogen and progesterone influence the risk of bladder cancer later in life remain unclear. In our study, other reproductive factors, such as age at menarche and at menopause, and BMI, low SES, and physical exercise were not associated with the incidence of bladder cancer. Our study has several strengths. First, our study included over 1 million postmenopausal women; the number of participants in the study is comparable to that of the existing observational study. Second, we used tibolone as the main drug for MHT. In previous studies, tibolone was not a mainstream prescription drug; however, it is the most prescribed medication in Korea. Third, we investigated most of the various combinations of drugs used for MHT, including several CEPMs (Angeliq®, Climen®, Clian®, and Femoston®). Finally, the study’s analysis was adjusted for age, BMI, SES, CCI, smoking and drinking history, physical exercise, and reproductive factors such as parity, age at menarche and at menopause, and the period from menopause to study inclusion. This study has some limitations. First, we could not confirm the detailed CEPM drug list due to the NHIC policy. Second, we used the HIRA dataset, which limits the availability of patient characteristics and information; therefore, we could not exclude the possibility of other unknown or residual confounding factors. Third, we could not perform a medical record review because the information provided by the HIRA dataset is categorized based on prescription and diagnostic codes. Conclusions Oral and topical oestrogen for MHT increased the incidence of kidney cancer in postmenopausal women. Meanwhile, tibolone significantly decreased the incidence of bladder cancer. Because of the study’s large population size of over 1 million, we expect the results of our study to be considered when treating postmenopausal women with MHT. Methods Study population and design South Korea has a universal system for health insurance coverage that serves approximately 98% (51 million) of Koreans, called the National Health Insurance, which the Korean National Health Insurance Corporation (NHIC) manages. This database includes detailed information, including age, sex, insurance type, socioeconomic status (SES), region of residency, types of medical institutions, prescription code, diagnostic code, and surgery code, on medical services insured for all diseases, excluding cosmetic surgery. Additionally, the NHIC recommends that employees and insured persons > 40 years old receive free cardiovascular health examinations every other year and that physical laborers undergo annual health examinations (“Health Security System”, n.d.); therefore, the NHIC has additional health examination data for these population groups. In 1999, the National Cancer Screening Program (NCSP) was promoted as part of the National Cancer Control Plan [ 44 ]. Currently, the NCSP provides free screening for liver, stomach, colorectal, cervical, and breast cancer to the whole population based on age (“Health Security System”, n.d.) [ 44 ]; hence, the NHIC retains self-questionnaire data on historical and cancer screening results. We performed a retrospective cohort study using health insurance data from the HIRA between January 1, 2002 and December 31, 2019. Diagnostic codes were extracted from the database using the International Classification of Diseases, 10th revision; surgery codes were extracted from the database using the Korea Health Insurance Medical Care Expenses (2012, 2016, 2019 version); and prescription codes were extracted from the database using the HIRA Drug Ingredients Codes. We selected postmenopausal women aged > 40 years from 2002 to 2011 to extract the case and control groups. Women on MHT were defined as those who received MHT for more than 6 months between 2002 and 2011, whereas women who did not use MHT were defined as those who were never prescribed MHT between 2002 and 2019. Women undergoing menopause in 2002 and those < 40 years old were excluded. Moreover, those who had diagnostic codes for any cancer (Cxx) or any urinary tract disease (N0–4) within the 180th day after the study inclusion date were excluded. Women with kidney cancer were defined as those who visited a medical institution three or more times with the diagnostic code for kidney cancer (C64), while women with bladder cancer were defined as those who visited a medical institution three or more times with the diagnostic code for bladder cancer (C67). MHT formulations were limited to cases where tibolone, combined oestrogen plus progestin by the manufacturer (CEPM), oral oestrogen alone, combined oestrogen plus progestin by the physician (CEPP), or topical oestrogen was prescribed. A detailed list of the medications can be found as Supplementary Table S1 online. Patients who sequentially received more than two MHT formulations were assigned to the subgroup of MHT formulation that was last used for more than 6 months. Patient characteristics, such as age, body mass index (BMI), Charlson comorbidity index (CCI), SES, and region of residency, and reproductive factors, including parity, age at menarche and at menopause, and the period from menopause to study participation date, as well as habits surrounding smoking, alcohol consumption, and physical exercise, were reviewed. The Asia-Pacific guidelines were followed when measuring BMI [ 45 ], and a low SES was defined as the patient having received medical aid as medical insurance. An urban area was defined as an administrative district that was a large city. The CCI was calculated using the diagnostic codes for diseases that were the cause of visits to medical institutions within 1 year before the participation date in our study [ 46 ]. Smoking history was divided into “never”, “past”, and “current”, and drinking history was classified according to the number of alcoholic drinks per week. Exercise strength was classified based on the frequency of exercise lasting 30 min. or more per week. Statistical Analyses The initiation date of treatment in the MHT and non-MHT groups was defined as the date when MHT was first prescribed and at which health examination was performed, respectively. All continuous variables were described as medians (25th, 75th percentile), while all categorical variables were described as numbers (percentage). A Cox proportional hazard analysis was used to determine the risk of urinary tract cancer. The last day of follow-up was defined as the date of death or the last confirmed date in the health insurance data. To confirm the robustness of this study, only cases wherein an obstetrician/gynaecologist prescribed MHT were selected and analysed. All statistics were two-tailed, and statistical significance was set at a P-value of < 0.05. If a value was missing, the listwise deletion method was applied. All statistical analyses in this study were conducted using the SAS Enterprise Guide, version 6.1 (SAS Institute, Inc., Cary, NC, USA). Ethics Statement The Institutional Review Board (IRB) of Sanggye Paik Hospital, Inje University, approved this study (committee reference number: 2020-08-002). The requirement for informed consent was waived by Institutional Review Board (IRB) of Sanggye Paik Hospital, Inje University. We confirm that all methods were performed in accordance with the relevant guidelines and regulations According to the NHIC privacy policy, all information that could identify individuals was removed, and analysis of the dataset was only possible through a virtual server within the NHIC. Therefore, we could export only the results for analysis while causing no harm to the study participants. Based on South Korea’s Bioethics and Safety Act, informed consent was not required. The HIRA accepts no responsibility for the study’s findings. Declarations Acknowledgements The authors thank the entire staff of the Department of Urology, Sanggye Paik Hospital, Inje University College of Medicine. Author Contributions (I) Conception and design: J-S Y (II) Administrative support: J-S Y, S-H Y, J H Y, J Y K (III) Provision of study materials and patients: J-S Y (IV) Collection and assembly of data: J-S Y (V) Data analysis and interpretation: J-S Y, J Y K (VI) Manuscript writing: J Y K (VII) Final approval of manuscript: All authors Data Availability Statement The datasets analysed during the current study are available from the corresponding author upon reasonable request. Competing Interests The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Cody, J.D., Jacobs, M.L., Richardson, K., Moehrer, B. & Hextall, A. Oestrogen therapy for urinary incontinence in post-menopausal women. 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Analysis of the prognostic relevance of sex-steroid hormonal receptor mRNA expression in muscle-invasive urothelial carcinoma of the urinary bladder. Virchows Arch. 474 , 209–17; https://doi.org/10.1007/s00428-018-2496-9 (2019). Li, P., Chen, J., Miyamoto, H. Androgen Receptor Signaling in Bladder Cancer. Cancers (Basel). 22: 9 (2017) Dobruch, J. et al . Gender and Bladder Cancer: A Collaborative Review of Etiology, Biology, and Outcomes. Eur Urol. 69 , 300–10 (2016). Miyamoto, H. et al . Expression of androgen and oestrogen receptors and its prognostic significance in urothelial neoplasm of the urinary bladder. BJU Int. 109 , 1716–26 (2012). Robinson, D. & Cardozo, L.D. The role of estrogens in female lower urinary tract dysfunction. Urology. 62 , 45–51 (2003). Hextall, A. Oestrogens and lower urinary tract function. Maturitas. 36 , 83–92 (2000). Aikawa, K. et al . The effect of ovariectomy and estradiol on rabbit bladder smooth muscle contraction and morphology. J Urol. 170 , 634–7 (2003). La Vecchia, C., Negri, E., D'Avanzo, B., Savoldelli, R. & Franceschi, S. Genital and urinary tract diseases and bladder cancer. Cancer Res. 51 , 629–31 (1991). Kantor, A.F. et al . Urinary tract infection and risk of bladder cancer. Am J Epidemiol. 119 , 510–5 (1984). Yu, Y., Shen, Z., Zhou, X. & Chen, S. Effects of steroid hormones on morphology and vascular endothelial growth factor expression in female bladder. Urology. 73 , 1210–7; https://doi.org/10.1016/j.urology.2008.10.050 (2009). Yang, X., Li, Y.Z., Mao, Z., Gu, P. & Shang, M. Effects of estrogen and tibolone on bladder histology and estrogen receptors in rats. Chin. Med. J. (Engl.). 122 , 381–5 (2009). Kloosterboer, H.J. Tissue-selectivity: the mechanism of action of tibolone. Maturitas. 48 Suppl 1, S30-40; https://doi.org/10.1016/j.maturitas.2004.02.012 (2004). Cumberbatch, M.G., Rota, M., Catto, J.W., La Vecchia, C. The Role of Tobacco Smoke in Bladder and Kidney Carcinogenesis: A Comparison of Exposures and Meta-analysis of Incidence and Mortality Risks. Eur Urol. 70 , 458–66 (2016). Koroukian, S.M., Bakaki, P.M., Raghavan, D. Survival disparities by Medicaid status: an analysis of 8 cancers. Cancer. 118 , 4271–9 (2012). Sharp, L. et al . Risk of Several Cancers is Higher in Urban Areas after Adjusting for Socioeconomic Status. Results from a Two-Country Population-Based Study of 18 Common Cancers. Journal of Urban Health. 91 , 510–25; https://doi.org/10.1007/s11524-013-9846-3 (2014). Yoo, K.Y. Cancer control activities in the Republic of Korea. Jpn. J. Clin. Oncol. 38 , 327–33; https://doi.org/10.1093/jjco/hyn026 (2008). Whoro, P. The Asia-Pacific perspective: redefining obesity and its treatment. Health Communications Australia . Sydney (2000). Quan, H. et al . Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am. J. Epidemiol. 173 , 676–82; https://doi.org/10.1093/aje/kwq433 (2011). Additional Declarations No competing interests reported. Supplementary Files SupplementarytableS1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2148280","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":143942511,"identity":"55d4b401-c094-49ce-8716-96e74af45087","order_by":0,"name":"Jin-Sung Yuk","email":"","orcid":"","institution":"Inje University Sanggye Paik Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin-Sung","middleName":"","lastName":"Yuk","suffix":""},{"id":143942513,"identity":"2c9c9c29-d1d4-4693-a028-04b854c18fd2","order_by":1,"name":"Sang-Hee Yoon","email":"","orcid":"","institution":"Inje University Sanggye Paik Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sang-Hee","middleName":"","lastName":"Yoon","suffix":""},{"id":143942515,"identity":"73c6b86e-1426-42bd-b228-3f9cc82c4986","order_by":2,"name":"Ji Hyeong Yu","email":"","orcid":"","institution":"Inje University Sanggye Paik Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Hyeong","lastName":"Yu","suffix":""},{"id":143942517,"identity":"fb5c6cf0-36f7-42bd-b2ec-70bec222d719","order_by":3,"name":"Jae Yoon Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFACxgYJEMUPJhkkZIjXIjkDooUHLERIE1iLwQ0Ih7AW+Yjkxhsfd9yTM77d/OzRjRoLHgbp5uMP8GkxvJHYbDnzTLGx2Z1j5sY5x4AOkzmWiNcWwxmJbdK8bQmJ224kmEnnsAG1SOQYEtbyF6hl84z0b9I5/0Ba8j/i94sEUAsjUMsGiRwz6dw2sC34vW/A87DZsvdMgrHEjZwy6dw+CR42iTTDGXhtaU9/eOPnjgQ5/hnp26RzvtXJ8UskP/iA15YDDGjxwIZPOdiWBnQto2AUjIJRMArQAQDO6EevkbBnzQAAAABJRU5ErkJggg==","orcid":"","institution":"Inje University Sanggye Paik Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Yoon","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2022-10-09 16:29:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2148280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2148280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27933624,"identity":"ab2cdb35-63c7-4ca3-9056-b36ce6a92298","added_by":"auto","created_at":"2022-10-18 14:40:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":501043,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for patient classification according to menopausal hormone therapy. Korea National Health Insurance Data, 2002–2019.\u003c/p\u003e\n\u003cp\u003eMHT: menopausal hormone therapy\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2148280/v1/47f57283fe52fd519c8794ee.png"},{"id":27933626,"identity":"ee515d14-9535-4b8d-961a-2b48b02d8452","added_by":"auto","created_at":"2022-10-18 14:40:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1012833,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Hazard ratios for risk of urogenital cancer according to type of menopausal hormone therapy drug. Korea National Health Insurance Data, 2002–2019. (b) Large-scale plots representing the results.\u003c/p\u003e\n\u003cp\u003eCI: confidence interval, HR: hazard ratio\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2148280/v1/c72f672c0cce6c945c77f846.png"},{"id":30303342,"identity":"42b8095b-6bd8-4699-8e9b-3b35eac85ed1","added_by":"auto","created_at":"2022-12-14 07:29:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1278095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2148280/v1/08a2819e-fd28-4d41-95ae-ea2f2a468427.pdf"},{"id":27933625,"identity":"745c1c29-5495-4181-9ffe-2a1f1a305709","added_by":"auto","created_at":"2022-10-18 14:40:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38115,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2148280/v1/819030753ed33854a324875b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association of menopausal hormone therapy with the incidence of urinary tract cancer: a national population-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMany women experience various symptoms of menopause, including vasomotor symptoms (sweating and hot flushes), vaginal symptoms (sexual discomfort and vaginal dryness), psychological problems (anxiety and low mood), frequent urinary infections, and urinary incontinence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which are caused by the natural decline in oestrogen and progesterone levels.\u003c/p\u003e \u003cp\u003eHence, menopausal hormone therapy (MHT) has been widely prescribed to alleviate symptoms. MHT comes in several formulations, the most common of which are oestrogen, combined oestrogen and progestogen, and tibolone, a synthetic steroid.\u003c/p\u003e \u003cp\u003eAlthough there are benefits to MHT use [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], several studies report that MHT can increase the risk of stroke, cardiovascular disease, cholecystitis, meningioma, breast cancer, and ovarian cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous reports reveal that MHT use is particularly associated with urologic cancer in women, including kidney [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and bladder cancer [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKidney cancer is the 16th most common cancer worldwide, accounting for 2.2% of all newly diagnosed cases of malignancy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Risk factors for kidney cancer include cigarette smoking, hypertension, obesity, and chronic kidney disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; however, the aetiology of the disease remains unknown. Bladder cancer is the ninth most frequently diagnosed cancer worldwide. In 2018, the estimated number of newly diagnosed cases of bladder cancer in men and women in the United States was 62,100 and 19,300, respectively [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The most common risk factors for bladder cancer are smoking and occupational exposure to aromatic amines [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, epidemiological studies have shown that the incidence of both types of urologic cancer is three- to four-fold times higher in men than in women [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which may be because more men smoke; however, the known risk factors are unlikely to explain the differences in the incidence of urologic cancer between men and women.\u003c/p\u003e \u003cp\u003eAnother possible factor is the difference in the levels of hormones between sexes, including hormonal changes after menopause and, particularly, the use of exogenous hormones for MHT.\u003c/p\u003e \u003cp\u003ePrevious research reports on the association between hormonal factors, including exogenous hormone use for MHT, and the incidence of urologic cancer in women, including kidney and bladder cancer; however, the results are inconsistent.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to evaluate the relationship between MHT and urologic cancer risk in women using data from the Korean Health Insurance Review and Assessment Service (HIRA).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of women on MHT\u003c/h2\u003e \u003cp\u003eAmong the 2,506,271 women who underwent national health examinations between 2002 and 2011 and after excluding those with urinary tract diseases or cancer, 557,031 and 204,130 were classified into the non-MHT and MHT groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean age and BMI were 56 (52\u0026ndash;62) years and 23.8 (21.9\u0026ndash;25.9) kg/m\u003csup\u003e2\u003c/sup\u003e, respectively. The follow-up periods for the non-MHT and MHT groups were 12.2 years and 13.4 years, respectively. Among the MHT group, 104,089 women were on tibolone, 65,597 on CEPM, 29,357 on oral oestrogen alone, 3,913 on CEPP, and 1,174 on topical oestrogen. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the detailed characteristics of all participants in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of women according to menopausal hormone exposure status at recruitment, Korea National Health Insurance Data, 2002\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-MHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOral oestrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e557,031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e104,089\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e65,597\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e29,357\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3,913\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,174\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e761,161\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 [52\u0026ndash;64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 [51\u0026ndash;58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 [50\u0026ndash;56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 [49\u0026ndash;57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 [51\u0026ndash;59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53 [50\u0026ndash;57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56 [52\u0026ndash;62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at inclusion (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026thinsp;~\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52,832 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,704 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,788 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,667 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e665 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e247 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93,903 (12.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269,365 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,612 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43,189 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,524 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,274 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e720 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e399,684 (52.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168,878 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,789 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,070 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,372 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e866 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e192 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e198,167 (28.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65,956 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,984 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e794 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69,407 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 [22.1\u0026ndash;26.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5 [21.8\u0026ndash;25.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.1 [21.5\u0026ndash;25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.7 [22-25.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.3 [21.6\u0026ndash;25.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.7 [22-25.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.8 [21.9\u0026ndash;25.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,547 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,710 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,330 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e428 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14,108 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188,340 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,595 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,798 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,538 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,583 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e436 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e270,290 (36.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23-24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144,971 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,732 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,594 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,255 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,097 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e200,952 (26.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178,877 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,093 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,014 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,887 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,039 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e372 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e234,282 (31.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,810 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,761 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,348 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e981 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28,017 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMid\u0026thinsp;~\u0026thinsp;high SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532,414 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100,037 (96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63,850 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28,504 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,821 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,138 (96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e729,764 (95.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,617 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,052 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,747 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e853 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31,397 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164,762 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,685 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,255 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,778 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,066 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e561 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e234,107 (30.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392,269 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70,404 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,342 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,579 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,847 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e613 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e527,054 (69.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375,267 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73,114 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48,200 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,107 (71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,798 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e795 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e521,281 (68.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102,894 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,370 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,097 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,105 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e698 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e199 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e139,363 (18.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78,870 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,605 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,300 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,145 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e417 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e180 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100,517 (13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 or no response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114,181 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,045 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,105 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,873 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e862 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e264 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e151,330 (19.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,275 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,689 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,505 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,135 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48,963 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330,424 (69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65,627 (70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43,480 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17,184 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,333 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e676 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e459,724 (69.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81,151 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,728 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,507 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,165 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e451 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101,144 (13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104,413 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,245 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,047 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,574 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e745 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e237 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e137,261 (18.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448,932 (81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86,894 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55,103 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23,373 (80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,137 (80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e919 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e618,358 (81.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76,978 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,353 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,086 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,551 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e523 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e233 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105,724 (13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161,668 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34,335 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,038 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,446 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,261 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e444 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e230,192 (30.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271,690 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,373 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,939 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,969 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,799 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e421 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e364,191 (52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46,695 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,028 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,534 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,391 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e330 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61,054 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507,290 (96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93,508 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59,150 (93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,744 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,606 (96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,063 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e691,361 (95.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,299 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,647 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,122 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e379 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,507 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,984 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,610 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,048 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,037 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22,811 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e450,396 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78,540 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48,955 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22,758 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,148 (83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e930 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e604,727 (83.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;2/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67,537 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,817 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,708 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,958 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e580 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e173 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e104,773 (14.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;6/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,907 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,408 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,654 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e516 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12,546 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,037 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e798 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,458 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical exercise (per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341,799 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58,669 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37,461 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,584 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,101 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e597 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e457,211 (62.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88,768 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,363 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,641 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,571 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e743 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e228 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127,314 (17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49,298 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,769 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,503 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,202 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e511 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e168 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72,451 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,335 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,866 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,532 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,001 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23,933 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,461 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,824 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,583 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,029 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e274 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46,252 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod from menopause to inclusion (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214,939 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57,705 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,930 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,163 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,931 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e592 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e333,260 (43.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122,455 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,240 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,963 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,584 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e965 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e316 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e170,523 (22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219,637 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,144 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,704 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,610 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,017 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e266 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e257,378 (33.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBMI: Body mass index, CCI: Charlson comorbidity index, MHT: menopausal hormone therapy, SES: socioeconomic status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are expressed as the number (%) or median [25th percentile, 75th percentile].\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIncidence Of Urologic Cancer In Women On Various Formulations Of Mht\u003c/h3\u003e\n\u003cp\u003eKidney cancer occurred in 997 (0.2%) women in the non-MHT group. In the MHT group, kidney cancer occurred in 190 (0.2%), 106 (0.2%), 75 (0.3%), 4 (0.1%), and 7 (0.6%) women treated with tibolone, CEPM, oral oestrogen alone, CEPP, and topical oestrogen, respectively.\u003c/p\u003e \u003cp\u003eAmong the women in the non-MHT group, bladder cancer occurred in 910 (0.2%) patients. In the MHT group, bladder cancer occurred in 104 (0.1%), 60 (0.1%), 35 (0.1%), 5 (0.1%), and 5 (0.4%) women treated with tibolone, CEPM, oral oestrogen alone, CEPP, and topical oestrogen, respectively.\u003c/p\u003e \u003cp\u003eThe median duration of hormone therapy in the MHT group was 23 (10\u0026ndash;55) months. Additionally, 77.2% of women in the MHT group received hormone therapy within 5 years after menopause (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of women on menopausal hormones, Korea National Health Insurance Data, 2002\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHT characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOral oestrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal MHT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian duration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 [11\u0026ndash;58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 [11\u0026ndash;57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 [10\u0026ndash;55]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78,686 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50,015 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24,397 (83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,413 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,114 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e157,625 (77.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,807 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,502 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,273 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e387 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33,026 (16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,596 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,080 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,687 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,479 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of previous other MHT (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101,557 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64,660 (98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,987 (98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,287 (84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,163 (99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e199,654 (97.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,244 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e858 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e466 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,903 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e573 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast dosage of Tibolone (per day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.25 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e983 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.5 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103,002 (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eover 5 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrescribed specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynaecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,073 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,160 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,120 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e908 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e263 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74,524 (36.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-gynaecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71,016 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36,437 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,237 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,005 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e911 (77.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e129,606 (63.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMHT: menopausal hormone therapy\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are expressed as the number (%) or median [25th percentile, 75th percentile].\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the Cox proportional hazard analysis that adjusted for variables such as age, the incidence of kidney cancer was significantly increased in women treated with oral oestrogen alone (hazard ratio [HR] 1.36, 95% confidence interval [CI]: 1.062\u0026ndash;1.735) and topical oestrogen (HR 2.84, 95% CI: 1.270\u0026ndash;6.344) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the incidences of kidney cancer among women treated with tibolone (HR 0.99, 95% CI: 0.846\u0026ndash;1.180), CEPM (HR 1.02, 95% CI: 0.830\u0026ndash;1.264), and CEPP (HR 0.55, 95% CI: 0.204\u0026ndash;1.456) were not different (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios for risk of urogenital cancer according to MHT drug type, Korea National Health Insurance Data, 2002\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.846\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.024 (0.83\u0026ndash;1.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.358 (1.062\u0026ndash;1.735)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545 (0.204\u0026ndash;1.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.839 (1.27\u0026ndash;6.344)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy of the renal pelvis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.735 (0.432\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.131 (0.628\u0026ndash;2.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.027 (1.133\u0026ndash;3.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0-5.28E\u0026thinsp;+\u0026thinsp;207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0-.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy of the ureter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769 (0.51\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.946 (0.57\u0026ndash;1.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.225 (0.695\u0026ndash;2.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625 (0.087\u0026ndash;4.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0-1.26E\u0026thinsp;+\u0026thinsp;194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.686 (0.548\u0026ndash;0.858)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809 (0.613\u0026ndash;1.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.856 (0.602\u0026ndash;1.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.832 (0.345\u0026ndash;2.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.591 (0.968\u0026ndash;6.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCI: confidence interval, HR: hazard ratio, MHT: menopausal hormone therapy\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eHRs were adjusted for age group, body mass index, socioeconomic status, region, Charlson comorbidity index, parity, age at menarche, age at menopause, smoking, alcohol consumption, physical exercise, and period from menopause to inclusion.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotably, tibolone significantly decreased the incidence of bladder cancer (HR 0.69, 95% CI: 0.548\u0026ndash;0.858) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas other formulations did not affect the incidence of bladder cancer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the dose-dependent analysis, tibolone had no preventive effect on bladder cancer when 1.25 mg (half dose) was given (HR 1.43, 95% CI: 0.357\u0026ndash;5.735) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociation Of Risk Factors With Urologic Cancer Development In Women On Mht\u003c/h3\u003e\n\u003cp\u003eIn the analysis that adjusted for variables, the incidence of kidney cancer increased with older age (\u0026ge;\u0026thinsp;70 years; HR 2.25, 95% CI: 1.54\u0026ndash;3.293), obesity (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e; HR 1.81, 95% CI: 1.388\u0026ndash; 2.367), current smoking status (HR 1.37, 95% CI: 1.014\u0026ndash;1.851), and exercise (5\u0026ndash;6 times/week; HR 1.38, 95% CI: 1.042\u0026ndash;1.822) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, living in a rural area (HR 0.88, 95% CI: 0.782\u0026ndash;0.99) and drinking alcohol (2 drinks/week; HR 0.77, 95% CI: 0.642\u0026ndash;0.918) decreased the risk of kidney cancer. Additionally, the reproductive factors of age at menarche and at menopause, as well as parity, were not associated with the incidence of kidney cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios for risk of urogenital cancer according to major variables, Korea National Health Insurance Data, 2002\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRenal cancer \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMalignancy of the renal pelvis \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMalignancy of the ureter \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eBladder cancer \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e(0.846\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003cp\u003e(0.432\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003cp\u003e(0.51\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003cp\u003e(0.548\u0026ndash;0.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by manufacturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;1.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003cp\u003e(0.628\u0026ndash;2.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003cp\u003e(0.57\u0026ndash;1.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003cp\u003e(0.613\u0026ndash;1.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral oestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003cp\u003e(1.062\u0026ndash;1.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.027\u003c/p\u003e \u003cp\u003e(1.133\u0026ndash;3.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.225\u003c/p\u003e \u003cp\u003e(0.695\u0026ndash;2.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003cp\u003e(0.602\u0026ndash;1.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined oestrogen plus progestin by physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003cp\u003e(0.204\u0026ndash;1.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0-5.28E\u0026thinsp;+\u0026thinsp;207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003cp\u003e(0.087\u0026ndash;4.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003cp\u003e(0.345\u0026ndash;2.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopical oestrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.839\u003c/p\u003e \u003cp\u003e(1.27\u0026ndash;6.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0-.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0-1.26E\u0026thinsp;+\u0026thinsp;194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.591\u003c/p\u003e \u003cp\u003e(0.968\u0026ndash;6.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at inclusion (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.449\u003c/p\u003e \u003cp\u003e(1.148\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.527\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;3.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.317\u003c/p\u003e \u003cp\u003e(1.182\u0026ndash;4.543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.787\u003c/p\u003e \u003cp\u003e(1.277-2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.732\u003c/p\u003e \u003cp\u003e(1.249\u0026ndash;2.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.094\u003c/p\u003e \u003cp\u003e(0.788\u0026ndash;5.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.888\u003c/p\u003e \u003cp\u003e(1.719\u0026ndash;8.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.141\u003c/p\u003e \u003cp\u003e(2.064\u0026ndash;4.781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003cp\u003e(1.54\u0026ndash;3.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.086\u003c/p\u003e \u003cp\u003e(1.406\u0026ndash;11.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.765\u003c/p\u003e \u003cp\u003e(3.221\u0026ndash;18.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.374\u003c/p\u003e \u003cp\u003e(3.408\u0026ndash;8.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003cp\u003e(0.39\u0026ndash;1.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003cp\u003e(0.35\u0026ndash;3.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003cp\u003e(0.024\u0026ndash;1.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003cp\u003e(0.737\u0026ndash;1.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23-24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.379\u003c/p\u003e \u003cp\u003e(1.188\u0026ndash;1.601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.489\u003c/p\u003e \u003cp\u003e(1.011\u0026ndash;2.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003cp\u003e(0.643\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003cp\u003e(0.856\u0026ndash;1.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.624\u003c/p\u003e \u003cp\u003e(1.413\u0026ndash;1.868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.414\u003c/p\u003e \u003cp\u003e(0.972\u0026ndash;2.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003cp\u003e(0.798\u0026ndash;1.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003cp\u003e(0.872\u0026ndash;1.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.813\u003c/p\u003e \u003cp\u003e(1.388\u0026ndash;2.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003cp\u003e(0.253\u0026ndash;1.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003cp\u003e(0.606\u0026ndash;1.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003cp\u003e(0.912\u0026ndash;1.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003cp\u003e(0.797\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003cp\u003e(0.31\u0026ndash;2.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003cp\u003e(0.493\u0026ndash;2.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003cp\u003e(0.742\u0026ndash;1.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.782\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003cp\u003e(0.759\u0026ndash;1.471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003cp\u003e(0.796\u0026ndash;1.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.709\u0026ndash;0.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003cp\u003e(0.922\u0026ndash;1.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.197\u003c/p\u003e \u003cp\u003e(0.833\u0026ndash;1.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.142\u003c/p\u003e \u003cp\u003e(0.858\u0026ndash;1.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.275\u003c/p\u003e \u003cp\u003e(1.097\u0026ndash;1.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003cp\u003e(0.835\u0026ndash;1.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003cp\u003e(0.739\u0026ndash;1.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003cp\u003e(0.734\u0026ndash;1.458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003cp\u003e(0.924\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.283\u003c/p\u003e \u003cp\u003e(0.938\u0026ndash;1.756)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.013\u003c/p\u003e \u003cp\u003e(0.763\u0026ndash;5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003cp\u003e(0.522\u0026ndash;1.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003cp\u003e(0.628\u0026ndash;1.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.297\u003c/p\u003e \u003cp\u003e(0.982\u0026ndash;1.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.544\u003c/p\u003e \u003cp\u003e(0.625\u0026ndash;3.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003cp\u003e(0.391\u0026ndash;1.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003cp\u003e(0.538\u0026ndash;0.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003cp\u003e(0.865\u0026ndash;1.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003cp\u003e(0.582\u0026ndash;3.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003cp\u003e(0.382\u0026ndash;1.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003cp\u003e(0.597\u0026ndash;1.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003cp\u003e(0.804\u0026ndash;1.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003cp\u003e(0.602\u0026ndash;1.572)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003cp\u003e(0.809\u0026ndash;1.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003cp\u003e(0.785\u0026ndash;1.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menopause (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003cp\u003e(0.752\u0026ndash;1.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003cp\u003e(0.826\u0026ndash;2.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003cp\u003e(0.559\u0026ndash;1.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003cp\u003e(0.931\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003cp\u003e(0.782\u0026ndash;1.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.874\u003c/p\u003e \u003cp\u003e(1.025\u0026ndash;3.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003cp\u003e(0.767\u0026ndash;1.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.262\u003c/p\u003e \u003cp\u003e(0.998\u0026ndash;1.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003cp\u003e(0.679\u0026ndash;1.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.828\u003c/p\u003e \u003cp\u003e(0.798\u0026ndash;4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003cp\u003e(0.675\u0026ndash;2.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003cp\u003e(0.756\u0026ndash;1.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003cp\u003e(0.356\u0026ndash;1.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003cp\u003e(0.084\u0026ndash;4.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003cp\u003e(0.051\u0026ndash;2.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003cp\u003e(0.619\u0026ndash;2.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e(1.014\u0026ndash;1.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003cp\u003e(0.462\u0026ndash;2.809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.817\u003c/p\u003e \u003cp\u003e(1.029\u0026ndash;3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.957\u003c/p\u003e \u003cp\u003e(1.463\u0026ndash;2.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (g/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;2/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003cp\u003e(0.642\u0026ndash;0.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003cp\u003e(0.444\u0026ndash;1.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003cp\u003e(0.533\u0026ndash;1.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003cp\u003e(0.754\u0026ndash;1.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;6/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003cp\u003e(0.748\u0026ndash;1.726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003cp\u003e(0.22\u0026ndash;3.641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003cp\u003e(0.412\u0026ndash;3.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003cp\u003e(0.195\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003cp\u003e(0.399\u0026ndash;1.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.869\u003c/p\u003e \u003cp\u003e(0.461\u0026ndash;7.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003cp\u003e(0.269\u0026ndash;4.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.681\u003c/p\u003e \u003cp\u003e(0.924\u0026ndash;3.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical exercise (per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.165\u003c/p\u003e \u003cp\u003e(1.007\u0026ndash;1.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003cp\u003e(0.527\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003cp\u003e(0.724\u0026ndash;1.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.813\u0026ndash;1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.069\u003c/p\u003e \u003cp\u003e(0.885\u0026ndash;1.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003cp\u003e(0.881\u0026ndash;2.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;1.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003cp\u003e(0.881\u0026ndash;1.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003cp\u003e(1.042\u0026ndash;1.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003cp\u003e(0.38\u0026ndash;2.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003cp\u003e(0.245\u0026ndash;1.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003cp\u003e(0.906\u0026ndash;1.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003cp\u003e(0.94\u0026ndash;1.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003cp\u003e(0.413\u0026ndash;1.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003cp\u003e(0.572\u0026ndash;1.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003cp\u003e(0.774\u0026ndash;1.249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod from menopause to inclusion (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003cp\u003e(0.904\u0026ndash;1.254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.347\u003c/p\u003e \u003cp\u003e(0.832\u0026ndash;2.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003cp\u003e(0.974\u0026ndash;2.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.219\u003c/p\u003e \u003cp\u003e(0.986\u0026ndash;1.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003cp\u003e(0.814\u0026ndash;1.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003cp\u003e(0.844\u0026ndash;3.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(1.039\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.504\u003c/p\u003e \u003cp\u003e(1.146\u0026ndash;1.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBMI: Body mass index, CCI: Charlson comorbidity index, CI: confidence interval, HR: hazard ratio, MHT: menopausal hormone therapy, SES: socioeconomic status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e HRs were adjusted for age group, body mass index, socioeconomic status, region, Charlson comorbidity index, parity, age at menarche, age at menopause, smoking, alcohol consumption, physical exercise, and period from menopause to inclusion.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe adjusted Cox proportional hazard analysis revealed that the incidence of bladder cancer was higher in older women (\u0026ge;\u0026thinsp;70 years; HR 5.37, 95% CI: 3.408\u0026ndash;8.475), current smokers (HR 1.96, 95% CI: 1.463\u0026ndash;2.619), and those included in the study after \u0026gt;\u0026thinsp;10 years from menopause (HR 1.50, 95% CI: 1.146\u0026ndash;1.975) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, living in rural areas (HR 0.81, 95% CI: 0.709\u0026ndash;0.926), parity (parity, 2; HR 0.71, 95% CI: 0.538\u0026ndash;0.925), and drinking alcohol (3\u0026ndash;6 drinks/week; HR 0.44, 95% CI: 0.195\u0026ndash;0.980) decreased the incidence of bladder cancer. BMI, low SES, age at menarche and at menopause, and physical exercise were not associated with the incidence of bladder cancer.\u003c/p\u003e\n\u003ch3\u003eIncidence Of Urologic Cancer For Various Mht Formulations According To Age\u003c/h3\u003e\n\u003cp\u003eIn the age-group analysis, oral oestrogen increased the risk of kidney cancer only in women aged 50\u0026ndash;59 years old (HR 1.38, 95% CI: 1.008\u0026ndash;1.883), while topical oestrogen increased the risk of kidney cancer only in women aged 60\u0026ndash;69 years old (HR 4.83, 95% CI: 1.198\u0026ndash;19.461).\u003c/p\u003e \u003cp\u003eTibolone significantly decreased the incidence of bladder cancer only in women aged 50\u0026ndash;59 years old (HR 0.69, 95% CI: 0.508\u0026ndash;0.934), whereas it only had a tendency to lower the incidence of bladder cancer in women aged 60\u0026ndash;69 years old (HR 0.69, 95% CI: 0.467\u0026ndash;1.016).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study reveals that the use and type of MHT might affect the incidence of urologic cancer among postmenopausal women; oral and topical oestrogen formulations significantly increased the incidence of kidney cancer in women on MHT. However, other types of MHT were not significantly associated with the incidence of kidney cancer.\u003c/p\u003e \u003cp\u003ePrevious studies reveal that the risk of kidney cancer in postmenopausal women differs depending on the type of MHT. Among the types of MHT, studies that describe the association of oestrogen alone with kidney cancer show conflicting results [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Molokwu et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] discovered that the risk of kidney cancer was significantly increased in women using oestrogen alone. Similarly, McCredie et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reveal that women using oestrogen alone had a significantly increased risk of kidney cancer. Additionally, Zhang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] performed a meta-analysis of previously reported case\u0026ndash;control and cohort studies and demonstrated that oestrogen alone was associated with an increased risk of kidney cancer, which is consistent with our findings.\u003c/p\u003e \u003cp\u003eTwo other studies report on the relationship between the risk of kidney cancer and combined oestrogen plus progestin; however, the relationships were nonsignificant [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Setiawan et al. demonstrate that among 106,036 women, those on combined oestrogen plus progestin therapy had a nonsignificant 27% increase in the risk of kidney cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Meanwhile, among a population of 118,219 United States nurses, another study reports that women using combined oestrogen plus progestin had a nonsignificant decrease in the risk of kidney cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe underlying mechanisms regarding oestrogen therapy affecting the risk of kidney cancer remain unclear; however, several studies have proposed potential mechanisms. Tanaka et al. discovered that oestrogen and progesterone receptors in women are expressed in both normal and cancerous kidney tissue [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which is also reported by other studies that suggest that adjusting endocrine functions can partially reduce the risk of kidney cancer. Moreover, several experimental studies have suggested that the estrogenic effect is related to the occurrence of renal cell tumours in animals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOestrogen alone is only prescribed for patients who have undergone a hysterectomy. The most common indications for a hysterectomy are uterine fibroids and adenomyosis. Therefore, there might be an association between uterine fibroids, adenomyosis, hysterectomies, and kidney cancer. Zucchetto et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] also report on an increased incidence of kidney cancer in patients who underwent a hysterectomy.\u003c/p\u003e \u003cp\u003eIn the analysis that adjusted for variables, the incidence of kidney cancer was associated with older age, obesity, and current smoking status, which are consistent with previously known factors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Contrastingly, the incidence of kidney cancer was lower in women living in rural areas.\u003c/p\u003e \u003cp\u003eObesity is associated with an increased risk of kidney cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A previous study reports that the proportion of obese adults is higher in rural areas than in urban areas [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, previous studies demonstrate that smoking is a significant risk factor for kidney cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with men and women who smoke having a 1.54 and 1.22 higher risk of the disease, respectively, than non-smokers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSadowski et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] discovered that residents in rural areas have a lower incidence of kidney cancer but a higher mortality from the disease than those in urban areas, which is consistent with our findings and may be attributed to a disparity in available diagnostic utilization and hospital access between rural and urban areas. One study has reported that many hospitals in rural areas lacked qualified neuroimaging modalities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, reproductive factors, such as parity, as well as age at menarche and at menopause, were not associated with the incidence of kidney cancer.\u003c/p\u003e \u003cp\u003eTibolone significantly decreased the incidence of bladder cancer, whereas other MHT formulations were not significantly associated with the incidence of bladder cancer. Consistent with previous studies is that CEPM did not significantly decrease the incidence of bladder cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch has suggested that MHT in postmenopausal women affects the risk of bladder cancer, but the results have been inconsistent. Several studies have uncovered no significant association between MHT and bladder cancer [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; however, a few cohort studies have reported that the risk of bladder cancer may vary depending on the MHT formulation [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have uncovered that oestrogen alone was not significantly associated with an increased risk of bladder cancer; however, there may still be an association, albeit nonsignificant [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Daugherty et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported on an inverse association between combined oestrogen plus progestin and bladder cancer, and Davis-Dao et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] discovered similar results. However, a meta-analysis by Xu et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reveals that combined oestrogen plus progestin was negatively but not significantly associated with bladder cancer. Additionally, McGrath et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] report that women on combined oestrogen plus progestin therapy did not have a significantly lower risk of bladder cancer compared with women not using combined oestrogen plus progestin. However, a 2020 study with longer observation periods and more participants conducted by the same researcher involved no inverse association between combined oestrogen plus progestin and the risk of bladder cancer.\u003c/p\u003e \u003cp\u003eThe exact mechanisms underlying the results of previous observational studies are unknown; however, previous experimental research has uncovered that sex hormonal signalling plays an important role in the incidence and progression of bladder cancer [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn underlying mechanism could be that sex hormones have important physiological effects in maintaining the structures and functions of the female lower urinary tract [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The female genital and lower urinary tracts have a common embryonic origin arising from the urogenital sinus; therefore, significant alterations of sex hormone levels, such as those occurring with pregnancy or menopause, can cause significant modifications in the lower urinary tract, causing various symptoms, including urgency, incontinence, and recurrent urinary tract infections [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, frequent and recurrent urinary tract infections may be followed by chronic irritation of the bladder epithelium, resulting in a significantly increased risk of bladder cancer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Yu et al. demonstrate that sex hormones are essential in maintaining the bladder\u0026rsquo;s structure and that both oestrogen and androgen can reverse bladder muscle atrophy caused by ovariectomy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, Xin et al. suggest that the deprivation of sex hormones negatively affects the bladder structure and histology, and that oestrogen administration can preserve bladder function by inhibiting collagen hyperplasia and increasing smooth muscle density [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, tibolone significantly decreased the incidence of bladder cancer. There have been few studies on the association between tibolone use in postmenopausal women and the risk of bladder cancer. Tibolone has estrogenic, progestogenic, and androgenic effects with differential metabolism in each tissue, making it unique for treating menopausal symptoms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The estrogenic effects of tibolone are primarily expressed in the vagina, bone tissue, and brain, with less expression in the endometrium.\u003c/p\u003e \u003cp\u003eConsidering the results of previous research, selective tissue-specific effects of various sex hormones through tibolone are thought to maintain the bladder\u0026rsquo;s structure and function, which may prevent urinary tract symptoms and infections and thus, significantly lower the risk of bladder cancer, which is consistent with our findings.\u003c/p\u003e \u003cp\u003eIn our study, older age and current smoking status significantly increased the incidence of bladder cancer, which is consistent with previously known factors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, living in rural areas and parity significantly decreased the incidence of bladder cancer. Previous studies have shown that smoking is the most important risk factor for developing bladder cancer, with a 50% increased risk in smokers [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilar to kidney cancer, several studies have uncovered that women in rural areas have a lower incidence of bladder cancer than those in urban areas [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which is consistent with our results. Previous studies on the relationship between reproductive factors and the risk of bladder cancer did not show consistent results; however, several studies report that parity was negatively associated with the risk of bladder cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePregnancy causes dramatic changes in oestrogen and progesterone levels, which may persist for months. However, the exact mechanisms by which oestrogen and progesterone influence the risk of bladder cancer later in life remain unclear. In our study, other reproductive factors, such as age at menarche and at menopause, and BMI, low SES, and physical exercise were not associated with the incidence of bladder cancer.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, our study included over 1\u0026nbsp;million postmenopausal women; the number of participants in the study is comparable to that of the existing observational study. Second, we used tibolone as the main drug for MHT. In previous studies, tibolone was not a mainstream prescription drug; however, it is the most prescribed medication in Korea. Third, we investigated most of the various combinations of drugs used for MHT, including several CEPMs (Angeliq\u0026reg;, Climen\u0026reg;, Clian\u0026reg;, and Femoston\u0026reg;). Finally, the study\u0026rsquo;s analysis was adjusted for age, BMI, SES, CCI, smoking and drinking history, physical exercise, and reproductive factors such as parity, age at menarche and at menopause, and the period from menopause to study inclusion.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we could not confirm the detailed CEPM drug list due to the NHIC policy. Second, we used the HIRA dataset, which limits the availability of patient characteristics and information; therefore, we could not exclude the possibility of other unknown or residual confounding factors. Third, we could not perform a medical record review because the information provided by the HIRA dataset is categorized based on prescription and diagnostic codes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOral and topical oestrogen for MHT increased the incidence of kidney cancer in postmenopausal women. Meanwhile, tibolone significantly decreased the incidence of bladder cancer. Because of the study\u0026rsquo;s large population size of over 1\u0026nbsp;million, we expect the results of our study to be considered when treating postmenopausal women with MHT.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and design\u003c/h2\u003e \u003cp\u003eSouth Korea has a universal system for health insurance coverage that serves approximately 98% (51\u0026nbsp;million) of Koreans, called the National Health Insurance, which the Korean National Health Insurance Corporation (NHIC) manages. This database includes detailed information, including age, sex, insurance type, socioeconomic status (SES), region of residency, types of medical institutions, prescription code, diagnostic code, and surgery code, on medical services insured for all diseases, excluding cosmetic surgery. Additionally, the NHIC recommends that employees and insured persons\u0026thinsp;\u0026gt;\u0026thinsp;40 years old receive free cardiovascular health examinations every other year and that physical laborers undergo annual health examinations (\u0026ldquo;Health Security System\u0026rdquo;, n.d.); therefore, the NHIC has additional health examination data for these population groups. In 1999, the National Cancer Screening Program (NCSP) was promoted as part of the National Cancer Control Plan [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Currently, the NCSP provides free screening for liver, stomach, colorectal, cervical, and breast cancer to the whole population based on age (\u0026ldquo;Health Security System\u0026rdquo;, n.d.) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]; hence, the NHIC retains self-questionnaire data on historical and cancer screening results.\u003c/p\u003e \u003cp\u003eWe performed a retrospective cohort study using health insurance data from the HIRA between January 1, 2002 and December 31, 2019. Diagnostic codes were extracted from the database using the International Classification of Diseases, 10th revision; surgery codes were extracted from the database using the Korea Health Insurance Medical Care Expenses (2012, 2016, 2019 version); and prescription codes were extracted from the database using the HIRA Drug Ingredients Codes.\u003c/p\u003e \u003cp\u003eWe selected postmenopausal women aged\u0026thinsp;\u0026gt;\u0026thinsp;40 years from 2002 to 2011 to extract the case and control groups. Women on MHT were defined as those who received MHT for more than 6 months between 2002 and 2011, whereas women who did not use MHT were defined as those who were never prescribed MHT between 2002 and 2019. Women undergoing menopause in 2002 and those\u0026thinsp;\u0026lt;\u0026thinsp;40 years old were excluded. Moreover, those who had diagnostic codes for any cancer (Cxx) or any urinary tract disease (N0\u0026ndash;4) within the 180th day after the study inclusion date were excluded.\u003c/p\u003e \u003cp\u003eWomen with kidney cancer were defined as those who visited a medical institution three or more times with the diagnostic code for kidney cancer (C64), while women with bladder cancer were defined as those who visited a medical institution three or more times with the diagnostic code for bladder cancer (C67).\u003c/p\u003e \u003cp\u003eMHT formulations were limited to cases where tibolone, combined oestrogen plus progestin by the manufacturer (CEPM), oral oestrogen alone, combined oestrogen plus progestin by the physician (CEPP), or topical oestrogen was prescribed. A detailed list of the medications can be found as Supplementary Table S1 online.\u003c/p\u003e \u003cp\u003ePatients who sequentially received more than two MHT formulations were assigned to the subgroup of MHT formulation that was last used for more than 6 months.\u003c/p\u003e \u003cp\u003ePatient characteristics, such as age, body mass index (BMI), Charlson comorbidity index (CCI), SES, and region of residency, and reproductive factors, including parity, age at menarche and at menopause, and the period from menopause to study participation date, as well as habits surrounding smoking, alcohol consumption, and physical exercise, were reviewed.\u003c/p\u003e \u003cp\u003eThe Asia-Pacific guidelines were followed when measuring BMI [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and a low SES was defined as the patient having received medical aid as medical insurance. An urban area was defined as an administrative district that was a large city.\u003c/p\u003e \u003cp\u003eThe CCI was calculated using the diagnostic codes for diseases that were the cause of visits to medical institutions within 1 year before the participation date in our study [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmoking history was divided into \u0026ldquo;never\u0026rdquo;, \u0026ldquo;past\u0026rdquo;, and \u0026ldquo;current\u0026rdquo;, and drinking history was classified according to the number of alcoholic drinks per week. Exercise strength was classified based on the frequency of exercise lasting 30 min. or more per week.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eThe initiation date of treatment in the MHT and non-MHT groups was defined as the date when MHT was first prescribed and at which health examination was performed, respectively. All continuous variables were described as medians (25th, 75th percentile), while all categorical variables were described as numbers (percentage).\u003c/p\u003e \u003cp\u003eA Cox proportional hazard analysis was used to determine the risk of urinary tract cancer. The last day of follow-up was defined as the date of death or the last confirmed date in the health insurance data. To confirm the robustness of this study, only cases wherein an obstetrician/gynaecologist prescribed MHT were selected and analysed.\u003c/p\u003e \u003cp\u003eAll statistics were two-tailed, and statistical significance was set at a P-value of \u0026lt;\u0026thinsp;0.05. If a value was missing, the listwise deletion method was applied. All statistical analyses in this study were conducted using the SAS Enterprise Guide, version 6.1 (SAS Institute, Inc., Cary, NC, USA).\u003c/p\u003e\n\u003ch3\u003eEthics Statement\u003c/h3\u003e\n\u003cp\u003e The Institutional Review Board (IRB) of Sanggye Paik Hospital, Inje University, approved this study (committee reference number: 2020-08-002). The requirement for informed consent was waived by Institutional Review Board (IRB) of Sanggye Paik Hospital, Inje University. We confirm that all methods were performed in accordance with the relevant guidelines and regulations\u003c/p\u003e \u003cp\u003eAccording to the NHIC privacy policy, all information that could identify individuals was removed, and analysis of the dataset was only possible through a virtual server within the NHIC.\u003c/p\u003e \u003cp\u003eTherefore, we could export only the results for analysis while causing no harm to the study participants. Based on South Korea\u0026rsquo;s Bioethics and Safety Act, informed consent was not required. The HIRA accepts no responsibility for the study\u0026rsquo;s findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the entire staff of the Department of Urology, Sanggye Paik Hospital, Inje University College of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(I) Conception and design: J-S Y\u003c/p\u003e\n\u003cp\u003e(II) Administrative support: J-S Y, S-H Y, J H Y, J Y K\u003c/p\u003e\n\u003cp\u003e(III) Provision of study materials and patients: J-S Y\u003c/p\u003e\n\u003cp\u003e(IV) Collection and assembly of data: J-S Y\u003c/p\u003e\n\u003cp\u003e(V) Data analysis and interpretation: J-S Y, J Y K\u003c/p\u003e\n\u003cp\u003e(VI) Manuscript writing: J Y K\u003c/p\u003e\n\u003cp\u003e(VII) Final approval of manuscript: All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCody, J.D., Jacobs, M.L., Richardson, K., Moehrer, B. \u0026amp; Hextall, A. 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Epidemiol. \u003cb\u003e173\u003c/b\u003e, 676\u0026ndash;82; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwq433\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwq433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"menopausal hormone therapy, urological cancer, oestrogen, kidney cancer, bladder cancer","lastPublishedDoi":"10.21203/rs.3.rs-2148280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2148280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates the relationship between menopausal hormone therapy (MHT) and the risk of urologic cancer in women. It was conducted for South Korea\u0026rsquo;s national population based on the National Health Insurance Service Database between January 2002 and January 2019. The types of MHT in this study included tibolone, combined oestrogen plus progestin by the manufacturer (CEPM) or physician (CEPP), and oral and topical oestrogen. Furthermore, select patient characteristics and reproductive factors were reviewed. We performed a Cox proportional hazard analysis to clarify the risk of urologic cancer associated with MHT. According to MHT types, 104,089 were treated with tibolone, 65,597 with CEPM, 29,357 with oral oestrogen, 3,913 with CEPP, and 1,174 with topical oestrogen. Among women on MHT, the incidence of kidney cancer was significantly increased with oral oestrogen (hazard ratio [HR] 1.36, 95% confidence interval [CI]: 1.062\u0026ndash;1.735) and topical oestrogen (HR 2.84, 95% CI: 1.270\u0026ndash;6.344), whereas other formulations were not associated with kidney cancer. Meanwhile, tibolone significantly decreased the incidence of bladder cancer (HR 0.69, 95% CI: 0.548\u0026ndash;0.858), whereas other formulations were not associated with bladder cancer. Our findings suggest that MHT in postmenopausal women affects the incidence of urologic cancers.\u003c/p\u003e","manuscriptTitle":"The association of menopausal hormone therapy with the incidence of urinary tract cancer: a national population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-18 14:40:54","doi":"10.21203/rs.3.rs-2148280/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b032c38c-6729-4ecb-878a-768a03f93ca7","owner":[],"postedDate":"October 18th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-14T07:29:28+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-18 14:40:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2148280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2148280","identity":"rs-2148280","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}