Risk for human papillomavirus-associated gynecologic cancer among women of childbearing age with rheumatic diseases: a population-based cohort study

Risk for human papillomavirus-associated gynecologic cancer among women of childbearing age with rheumatic diseases: a population-based cohort 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 Research Article Risk for human papillomavirus-associated gynecologic cancer among women of childbearing age with rheumatic diseases: a population-based cohort study Jisoo Lee, In-Woon Baek, Hyunsun Lim, Min Kyung Chung, Pil Gyu Park, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4884521/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Evaluate the risk of human papillomavirus (HPV)-associated gynecologic cancer in women with rheumatic diseases (RD) during their childbearing years. Methods Using Korean National Health Insurance Service-National Health Information Database data (2011−2021), we conducted a cohort study of 40,514 women with RD and 199,366 women without RD aged 20–49 years. The RD cohort included 9,932 with systemic lupus erythematosus (SLE), 23,731 with seropositive rheumatoid arthritis (SPRA), and 6,851 with ankylosing spondylitis (AS). Incidence rates and hazard ratios for HPV-associated gynecologic cancer, including cervical intraepithelial neoplasia grade 3, and cervical, vaginal, and vulva cancers, were estimated using Cox regression. Results Over the mean (standard deviation) follow-up period of 67.5 (37.7) months, the incidence rate of HPV-associated gynecologic cancer was 111.5/100,000 person-years in the RD cohort and 73.2/100,000 person-years in the non-RD cohort. Among the RD subcohorts, the incidence rate/100,000 person-years of HPV-associated gynecologic cancer were higher in SLE (223.6) and SPRA (83.1), and lower in AS (69.1) compared with non-RD. The fully adjusted hazard ratio for HPV-associated gynecologic cancer was higher in the RD cohort (2.95 [95% CI 2.44–3.57]) and all the RD subcohorts (SLE 1.85 [95% CI 1.33–2.57], SPRA 4.10 [95% CI 3.03–5.55] and AS 1.91 [95% CI 1.06–3.43]). After adjusting for comorbidities and medication use, hazard ratios increased in SPRA and AS but decreased in SLE. Conclusion Korean women of childbearing age with RD have a threefold increased risk for HPV-associated gynecologic cancer compared with those without RD. The risk may be influenced by comorbidities and medication use in SLE. Improved screening strategies are needed for these women. Rheumatic diseases Human papillomavirus gynecologic cancer Childbearing age Hazard ratio Figures Figure 1 Figure 2 Background Human papillomavirus (HPV) is the most prevalent sexually transmitted infection in the general population [ 1 ]. The global prevalence rate of genital HPV infection in women is 2−44%, with the highest rates of incident and persistent infections observed in women aged 24−34 years [ 1 ]. HPV is a major cause of cervical cancer, which is one of the leading cause of cancer-related deaths among women globally [ 2 ]. Persistent high-risk HPV infection is a major risk factor for development of HPV-associated gynecologic cancers in women. However, behavioral factors and comorbid conditions, such as risky sexual behaviors, concomitant sexually transmitted infections, tobacco smoking, and immunosuppression also contribute to the increased risk for HPV-associated gynecologic cancer [ 3 – 7 ]. Immunocompromised women with rheumatic diseases (RD) have been reported to have a higher risk for gynecologic cancer associated with HPV infection. Several studies have demonstrated that women with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) face an increased risk for developing high-grade cervical dysplasia and cervical cancer [ 8 – 13 ], and vaginal and vulva cancers [ 14 ]. Furthermore, immunosuppressive medications used to treat severe SLE, such as cyclophosphamide, mycophenolate mofetil, azathioprine, and high doses of corticosteroids can increase the risk for cervical dysplasia in a cumulative dose- dependent manner, resulting in persistent HPV infection [ 7 , 15 – 18 ]. Moreover, RD, such as RA, SLE, and ankylosing spondylitis (AS), frequently occur in women of childbearing age [ 19 – 21 ], however, their risk for HPV-associated gynecologic cancer is not yet fully understood. The objective of this study was to evaluate the risk of HPV-associated gynecologic cancer and to determine the impact of comorbidities and medication use on this risk in Korean women with RD during their childbearing years, compared to those without RD. Methods Study design and data source A cohort study was conducted using data from the Korean National Health Insurance Service-National Health Information Database (NHIS-NHID) between 2009 and 2021. The Korean NHIS is a single insurer that provides coverage for most of the Korean population. The NHIS-NHID is composed of five databases, namely the eligibility, national health screening, health care utilization, long-term care insurance, and health care provider databases, which include information on income-based insurance contributions, residential districts, national health screening, inpatient and outpatient health care utilization (diagnosis and treatment), claims data, and health care providers [ 22 ]. For this study, data collected between January 1, 2009 and December 31, 2021 was used. In 2009, the Republic of Korea subsidized medical expenses for patients with rare and intractable diseases through a copayment assistance policy, the Individual Copayment Beneficiaries Program (ICBP); RD, including SLE, seropositive RA (SPRA), and AS were designated as rare diseases covered by this program. Under the ICBP, the NHIS established a registration program that included codes for the targeted diseases classified according to the Korean Standard Classification of Diseases (KCD)-7 (based on the International Classification of Diseases 10 th revision [ICD-10]), date of definitive diagnosis, and tests performed to confirm the diagnosis. Data from January 1, 2009 was used with the assumption that all patients with SPRA, SLE, and AS were accurately coded, as the ICBP registration required the classification criteria for a definitive diagnosis. This study protocol was approved by the Institutional Review Board of the National Health Insurance Service Ilsan Hospital, South Korea (NHIMC 2024-01-007), and conducted according to the principles of the Declaration of Helsinki. Since the database used in this study contains anonymized data for research purposes, informed consent was not required. Study cohort Women of childbearing age (defined as women between the ages of 20 and 49 years), we selected women with and without RD were selected. RD cohort included SLE, SPRA, and AS, identified with the diagnostic codes of M05, M32, and M45 based on the ICD-10 codes in the NHIS-NHID (2011−2021). The women with SLE, SPRA, and AS were included within the RD cohort as subcohorts, as they are representative diseases that occur frequently in women of childbearing age. The start of the follow-up period (i.e. the index date) was defined as the date of the first RD code identification. Women with HPV-associated gynecologic cancer within the 12-months before the index date were excluded. The HPV-associated gynecologic cancer included cervical intraepithelial neoplasia grade 3, and cervical, vaginal, vulva cancers identified with the ICD-10 codes D06, C53, C52, and C519, respectively. A non-RD cohort of women of childbearing age who did not have RD, including SLE, SPRA and AS in the NHIS-NHID (2011−2021) database, was identified for comparison with the RD cohort. Women in the non-RD cohort were age and index date matched with women in the RD cohort, and were randomly sampled in a 1:5 ratio. In the non-RD cohort, women matched by age and index date with the women with SLE, SPRA, or AS were selected to serve as controls for each RD subcohort. Women in the RD and non-RD cohorts were followed up until the earliest occurrence of any of the following events: development of HPV-associated gynecologic cancer, the end of the study database (2021), or death. Outcome measures The primary outcome measure was the development of HPV-associated gynecologic cancer. In addition, variables potentially associated with the risk for HPV-associated gynecologic cancer were assessed at the baseline and during the follow-up period. At the baseline, age, income, medical insurance, residential district, comorbidities, smoking status and preventive medical service utilization were assessed. The age, income, medical insurance, and residential district data were obtained at the index date. Comorbidities, including hypertension, diabetes mellitus, hyperlipidemia, and cancer, were identified with the ICD-10 codes I10-12/15, E10-14, E78, and C*, between 1 and 24 months before the index date. Smoking and preventive medical service utilization data were obtained from the NHIS health screening database within 24 months closest to the index date. Variables for the preventive medical service utilization included NHIS health screening and NHIS Papanicolaou (Pap) smear recipients. During the follow-up period, the same comorbidities and preventive medical service utilization data assessed at the baseline were evaluated in addition to the use of medication and health care utilization. Medications included nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, hydroxychloroquine, cyclophosphamide, immunosuppressants, and biologics. Use of immunosuppressants included methotrexate, leflunomide, azathioprine, cyclosporin, tacrolimus, mizoribine, and mycophenolate mofetil. The number of immunosuppressants used was also assessed. Biologics included etanercept, adalimumab, infliximab, golimumab, abatacept, tocilizumab, rituximab, ustekinumab, secukinumab, ixekizumab, tofacitinib, baricitinib, and upadacitinib. Variables included in health care utilization were the total number of outpatient department (OPD) and obstetrics and gynecology (OB/GYN) OPD visits during the follow-up period. Statistical analysis A descriptive analysis was performed to summarize the baseline and follow-up characteristics of the cohorts. Categorical variables were presented as frequencies and percentages, and continuous variables were presented as the mean ± standard deviation (SD). The baseline and follow-up characteristics of the RD and non-RD cohorts and the RD subcohorts and their controls were compared using the chi-square test for categorical variables and student t-test for continuous variables. The incidence rates (IRs) of HPV-associated gynecologic cancer in the RD and non-RD cohorts and the RD subcohorts were calculated per 100,000 person-years (PYs) with a 95% confidence interval (CI) during the follow-up period. Kaplan−Meier curves were plotted for the cumulative incidence of HPV-associated gynecologic cancer in the RD and non-RD cohorts and the RD subcohorts. Unadjusted and multivariate Cox proportional hazard models, which adjusted for multiple potential confounders associated with HPV infection were used to compare the risk of HPV-associated gynecologic cancer in the RD cohort and subcohorts with the non-Rd or RD subcohort controls, respectively. All statistical analyses were performed using Statistical Analysis Software version 9.4 (SAS Institute Inc., Cary, NC, USA). A p < 0.05 was considered statistically significant. Results Characteristics of the study cohorts The study included 41,514 women of childbearing age with RD, including 9,932 women with SLE, 23,731 with SPRA, 6,851 with AS, and 199,366 women without RD. Table 1 shows the baseline characteristics of the study cohorts. The mean ± SD duration of the follow up was similar in the RD and non-RD cohorts, 67.5 ± 37.7 and 67.7 ± 37.7 months, respectively. Since age was matched, the mean age ± SD was similar in the RD (37.5 ± 8.8) and non-RD (37.4 ± 8.8 years) cohorts; however, mean age was younger in the SLE (33.2 ± 9.4) and AS (34.4 ± 8.5), and older in the SPRA subcohort (40.1 ± 7.4). In addition, the most common ages in the SLE, SPRA, and AS subcohorts were between 20−24 years, 45−49 years, and 35−39 years, respectively. The RD cohort included more women in the lower income segment and on medical aid compared with the non-RD cohort. Comorbidities and preventive medical service utilization were higher in the RD compared with the non-RD cohort. Smoking status was not significantly different between the RD and non-RD groups. Table 1 Baseline characteristics of the study cohort RD* SLE SPRA AS Non-RD* N 40,514 9,932 23,731 6,851 199,366 Follow-up period, months 67.5 ± 37.7 67.5 ± 37.4 69.3 ± 37.7 60.8 ± 37.8 67.7 ± 37.7 Age, years Mean ± SD 37.5 ± 8.8 33.2 ± 9.4 40.1 ± 7.4 34.4 ± 8.5 37.4 ± 8.8 20−24 4,874 (12.0) 2,534 (25.5) 1,228 (5.2) 1,112 (16.2) 24,204 (12.1) 25−29 3,820 (9.4) 1,292 (13.0) 1,377 (5.8) 1,151 (16.8) 18,892 (9.5) 30−34 4,931 (12.2) 1,427 (14.4) 2,357 (9.9) 1,147 (16.7) 24,150 (12.1) 35−39 7,018 (17.3) 1,556 (15.7) 4,235 (17.9) 1,227 (17.9) 34,429 (17.3) 40−44 8,956 (22.1) 1,649 (16.6) 6,137 (25.9) 1,170 (17.1) 44,068 (22.1) 45−49 10,915 (26.9) 1,474 (14.8) 8,397 (35.4) 1,044 (15.2) 53,623 (26.9) Income level †, segment 1−3 11,513 (28.4) 2,929 (29.5) 6,665 (28.1) 1,919 (28.1) 53,570 (26.9) 4−7 14,389 (35.5) 3,472 (35.0) 8,435 (35.5) 2,482 (36.2) 72,979 (36.6) 8−10 13,574(33.5) 3,279 (33.0) 8,053 (33.9) 2,242 (32.7) 67,861 (34.0) Insurance Employee 28,050 (69.2) 6,930 (69.8) 16,225 (68.4) 4,895 (71.5) 139,004 (69.7) Self-employed 10,700 (26.4) 2,439 (24.6) 6,605 (27.83) 1,656 (24.2) 56,621 (28.4) Medical-aid 1,764 (4.35) 563 (5.7) 901 (3.8) 300 (4.4) 3,741 (1.9) Residential district City, province 8,106 (20.0) 2,137 (21.5) 4,338 (18.3) 1,631 (23.8) 41,705 (20.9) Metropolitan city 10,388 (25.6) 2,507 (25.2) 6,199 (26.1) 1,682 (24.6) 50,906 (25.5) Special city 22,020 (54.4) 5,288 (53.2) 13,194 (55.6) 3,538 (51.6) 106,755 (53.6) Comorbidities ‡ Hypertension 3,605 (8.9) 1,430 (14.4) 1,764 (7.4) 411 (6.0) 8,812 (4.42) DM 2,992 (7.39) 815 (8.21) 1,724 (7.3) 453 (6.61) 8,031 (4.03) Hyperlipidemia 12,598 (31.1) 3.496 (35.2) 7,124 (30.0) 1,978 (28.87) 24,033 (12.05) Cancer 1.757 (4.34) 516 (5.2) 967 (4.1) 274 (4.0) 5,809 (2.91) Smoking § None 22,604 (90.5) 4,450 (89.4) 14,597 (91.2) 3,557 (89.0) 108,350 (90.9) Ex-smoker 958 (3.8) 211(4.2) 573 (3.6) 174 (4.4) 4,390 (3.7) Current smoker 1,428 (5.7) 319 (6.4) 841 (5.3) 268 (6.7) 6,500 (5.5) Preventive medical service utilization § NHIS health screening recipients 18,328 (45.2) 3,579 (36.0) 11,825 (49.8) 2,922 (42.7) 84,991 (42.6) NHIS Pap smear recipients 15,800 (39) 3,014 (30.4) 10,252 (43.2) 2,534 (37.0) 71,490 (35.9) Categorial values are provided as n (%) and numerical quantitative data are provided as the mean ± SD. Variables were obtained at the index date unless otherwise indicated. *RD and non-RD cohorts were age- and index date-matched, RD include SLE, SPRA, and AS. † Income levels were determined according to the decile method (8–10 refers to the low-income group) ‡ Comorbidities were identified with diagnostic code based on the International Classification of Diseases 10th revision (ICD-10) between 1 and 24months before the index date. § Smoking and preventive medical service utilization data were obtained from NHIS Health Screening Examination Database within 24 months closest to the index date. RD, rheumatic diseases; SLE, systemic lupus erythematosus; SPRA, seropositive rheumatoid arthritis; AS, ankylosing spondylitis; DM, diabetes mellitus; NHIS, National Health Insurance Service; Pap, Papanicolaou. Characteristics recorded during the follow-up period are shown in Table 2 . All comorbidities and medication use were higher in RD compared with the non-RD. The same pattern of comorbidities and medication use was observed in the RD subcohorts compared with their controls, except for cyclophosphamide use in the AS subcohort, which was not significantly different compared with the control. The total number ± SD of OPD and OB/GYN OPD visits were higher in the RD compared with the non-RD cohort (23.0 ± 20.6 vs 11.3 ± 12.6 and 1.5 ± 3.8 vs 1.4 ± 3.3, respectively) (all p = < 0.0001). The number ± SD of Pap smear received/10 years was also higher in the RD compared with the non-RD cohort (1.13 ± 2.12 vs 1.07 ± 2.09, p = < 0.0001). However, the number ± SD of NHIS health screening received/10 years was not significantly different between the RD and the non-RD cohorts (2.17 ± 3.25 vs 2.17 ± 3.30, p = 0.927). Among the RD subcohorts, the total number of OPD and OB/GYN OPD visits/year were higher in the all RD subcohorts compared with their controls, with exception of total number of OB/GYN visits/year in SPRA, which was similar compared with the control. The number of NHIS health screening and Pap smear received showed differences among the RD subcohorts. The number of NHIS health screening received/10 years was lower in the SLE and higher in SPRA, but not different in the AS group. In contrast, the number of Pap smear received/10 years was higher in the SPRA and AS subcohorts, but not different in the SLE group compared with their controls. Table 2 Comorbidities, medication, and health care and preventive medical service utilization during the follow-up period RD* Non-RD* P value SLE* SLE control* P value SPRA* SPRA control* P value AS* AS control* P value N 40,514 199,366 9,932 48,958 23,731 116,647 6,851 33,761 Comorbidities Hypertension 6,662 (16.4) 16,208 (8.1) < .0001 2,924 (29.4) 3,136 (6.4) < .0001 3,128 (13.2) 11,164 (9.6) < .0001 610 (8.9) 1,908 (5.7) < .0001 DM 7,308 (18.0) 21,276 (10.7) < .0001 1,873 (18.9) 4,394 (9.0) < .0001 4,423 (18.6) 14,058 (12.1) < .0001 1,012 (14.8) 2,824 (8.4) < .0001 Hyperlipidemia 20,016 (49.4) 54,004 (27.1) < .0001 4,444 (44.7) 11,921 (24.4) < .0001 12,426 (52.4) 34,394 (29.5) < .0001 3,146 (45.9) 7,689 (22.8) < .0001 Cancer 3,650 (9.0) 11,733 (5.9) < .0001 1,013 (10.2) 2,502 (5.11) < .0001 2,152 (9.1) 7,621 (6.5) < .0001 485 (7.1) 1610 (4.8) < .0001 Medication use NSAIDs 32,218 (79.5) 48,182 (24.2) < .0001 4,598 (46.3) 10,347 (21.1) < .0001 21,413 (90.2) 31,564 (27.1) < .0001 6,209 (90.6) 6,217 (18.6) < .0001 Corticosteroids 29,045 (71.7) 9,383 (4.7) < .0001 7,697 (77.5) 2,087 (4.3) < .0001 18,978 (80.0) 6,060 (5.2) < .0001 2,370 (34.6) 1,236 (3.7) < .0001 Hydroxychloroquine 23,904 (59.0) 447 (0.2) < .0001 8,527 (85.9) 75 (0.2) < .0001 15,196 (64.0) 310 (0.3) < .0001 181 (2.6) 62 (0.2) < .0001 Cyclophosphamide 1,166 (2.9) 804 (0.4) < .0001 965 (9.7) 145(0.3) < .0001 176 (0.7) 567 (0.5) < .0001 25 (0.4) 92 (0.3) .193 Immunosuppressant † 23,017 (56.8) 1,135 (0.6) < .0001 4,808 (48.4) 278 (0.6) < .0001 17,516 (73.8) 704 (0.6) < .0001 693 (10.1) 153 (0.5) < .0001 No. of immunosuppressant † < .0001 < .0001 < .0001 2 2,480 (6.1) 27 (0.01) 459 (4.6) 7 (0.01) 2,004 (8.4) 20 (0.02) 17 (0.3) 0 Biologics ‡ 5,533 (13.7) 164 (0.08) < .0001 131 (1.3) 38 (0.08) < .0001 3,381 (14.3) 110 (0.09) < .0001 2,021 (29.5) 16 (0.05) < .0001 Health care utilization No of total OPD visits/year 23.0 ± 20.6 11.3 ± 12.6 < .0001 24.8 ± 21.8 10.5 ± 11.3 < .0001 22.0 ± 18.5 11.8 ± 13.4 < .0001 23.9 ± 25.2 10.6 ± 11.7 < .0001 No of OB/GYN OPD visits/year, 1.5 ± 3.8 1.4 ± 3.3 < .0001 1.8 ± 5.0 1.5 ± 3.5 < .0001 1.4 ± 3.1 1.3 ± 3.1 .182 1.8 ± 3.6 1.7 ± 3.5 < .0001 Preventive medical service utilization No. of NHIS health Screening received /10 years 2.17 ± 3.25 2.17 ± 3.30 .927 1.56 ± 2.88 1.73 ± 3.05 < .0001 2.48 ± 3.37 2.42 ± 3.39 .022 1.97 ± 3.19 1.94 ± 3.24 .395 No. of NHIS Pap smear received/10 years 1.13 ± 2.12 1.07 ± 2.09 < .0001 0.78 ± 1.84 0.80 ± 1.88 .571 1.32 ± 2.22 1.24 ± 2.19 < .0001 0.96 ± 2.06 0.88 ± 2.00 .003 Categorial values are provided as n (%) and numerical quantitative data are provided as mean ± SD. *RD and non-RD, SLE and SLE control, SPRA and SPRA control, and AS and AS control are age- and index date-matched, RD include SLE, SPRA, and AS. † Immunosuppressants include MTX, leflunomide, azathioprine, cyclosporin, tacrolimus, mizoribine, MMF. ‡ Biologics include etanercept, adlimumab, infliximab, golimumab, abatacept, tocilizumab, rituximab, ustekinumab, secukinumab, ixekinumab, tofacitinib, baricitinib, upadacitinib. RD, rheumatic diseases; SLE, systemic lupus erythematosus; SPRA, seropositive rheumatoid arthritis; AS, ankylosing spondylitis; NSAIDs nonsteroidal anti-inflammatory drugs, NHIS, National Health Insurance Service; Pap, Papanicolaou. Incidence rates and cumulative incidence of HPV-associated gynecologic cancer The follow-up period was 67.5 ± 37.7 months, during which the IR of HPV-associated gynecologic cancer between the cohorts was 111.5/100,000 PYs in the RD and 73.2/100,000 PYs in the non-RD cohort. Among the RD subcohorts, IR/100,000 PYs of HPV-associated gynecologic cancer was higher in women with SLE (223.6) and SPRA (83.1); in contrast, it was lower in women with AS (69.1) compared with that of the non-RD group (Fig. 1 A). Kaplan−Meier curves comparing the cumulative incidence of HPV-associated gynecologic cancer between the RD and non-RD cohorts revealed similar results, showing an increased risk for HPV-associated gynecologic cancer in the RD cohort, with the highest cumulative incidence observed in the SLE group (Fig. 1 B). Risk of HPV-associated gynecologic cancer The unadjusted hazard ratios (HRs) (95% [CI]) for HPV associated gynecologic cancer were raised in the RD (1.83 [1.42−2.6]), SLE (2.00 [1.15−3.45]), and SPRA (2.68 [1.81−3.97]), but not increased in the AS group (0.77 [0.44−1.36]). After full adjustment for possible confounders, the HRs for HPV associated gynecologic cancer were increased in the RD cohort (2.95 [95% CI 2.44−3.57]) and all RD subcohorts (SLE 1.85 [95% CI 1.33−2.57], SPRA 4.10 [95% CI 3.03−5.55], and AS 1.91 [95% CI 1.06−.43]). In the RD subcohorts, the HR was attenuated in the SLE, but increased in the SPRA and AS groups after adjustment for comorbidities and medication use during the follow-up period (Fig. 2 ). Discussion This is the first study to evaluate the risk for HPV-associated gynecologic cancer among women of childbearing age with RD. After adjusting for possible confounders, this study found that Korean women of childbearing age with RD have a threefold increased risk for HPV-associated gynecologic cancer compared with those without RD. This study demonstrated that the IR of HPV-associated gynecologic cancer in Korean women of childbearing age with RD was 111.5/100,000 PYs, which is approximately 1.6 times higher than the IR of age-matched women without RD (73.2/100,000 PYs). A direct comparison of the IR of HPV-associated gynecologic cancer between women of childbearing age with RD with RD of all ages is challenging. However, it has been reported that women aged ≥ 18 years with systemic inflammatory diseases have an IR of 94.2/100,000 PY of high-grade cervical dysplasia and cervical cancer [ 8 ]; this rate is lower compared with the IR of HPV-associated gynecologic cancer in women of childbearing age with RD found in this study. Additionally, after adjusting for potential confounders such as demographics, preventive medical service utilization, comorbidities, and medication use, this study found that women of childbearing age with RD have a 2.95 times greater risk for developing HPV-associated gynecologic cancer, whereas Kim SC et al. [ 8 ] reported that women aged ≥ 18 years with SLE and RA had a 1.5 times increased risk of high-grade cervical dysplasia and cervical cancer compared to those without systemic inflammatory diseases. These findings suggest that women of childbearing age with RD seems to be at a higher risk compared with women of all ages with RD. In addition, this study found that women of childbearing age with SPRA have the highest risk for HPV-associated gynecologic cancer with an HR of 2.95, followed by those with AS and SLE (HRs of 1.91 and 1.85, respectively). However, Kim SC et al. [ 8 ] reported a higher risk of high-grade cervical dysplasia and cervical cancer in women with SLE (HR 1.53) compared with women with RA (HR 1.49). This discrepancy may be because different age groups included in the two studies. While this study focused solely on women of childbearing age, the study by Kim SC et al. [ 8 ] included women across all age groups. Women over 65 years of age are more likely to experience long-term persistence of HPV infection [ 23 ], increasing their likelihood of developing cervical cancer [ 24 ]. In this study, women with SPRA had the highest mean age among the women of childbearing age, while those with SLE had the lowest mean age, which might have contributed to the increased risk for HPV-associated gynecologic cancer in women with SPRA. However, considering all age groups, the lifetime risk for women with SLE seems to be higher than for women with RA. The multivariable analysis examined the impact of medication use and comorbidities on the risk for HPV-associated gynecologic cancer in women with RD of childbearing age; we found that for women with SLE, the use of medications and the presence of associated comorbidities during the follow-up period significantly increased the risk for developing HPV-associated gynecologic cancer. Women of childbearing age with SLE have been reported to have greater burden of comorbidities and immunosuppressive drug use compared with those with SPRA and AS [ 25 ]. This is because SLE is a multisystem disease that necessitates immunosuppressive medication for treating severe organ involvement [ 26 ]. Our findings are supported by previous studies showing that women with SLE who are exposed to immunosuppressive have a higher risk of cervical dysplasia, and vaginal and vulva cancers [ 15 – 18 ]. Notably, the presence of comorbidities and the use of medications appeared to decrease the risk of HPV-associated gynecologic cancer in women with SPRA and AS. This may be attributed to the widespread use of NSAIDs for managing inflammatory arthritis in patients with RA and AS. The COX-2 gene has been implicated in early cervical carcinogenesis and tumor progression [ 27 , 28 ] and the result of a few small clinical trials have suggested that COX-2 inhibitors may play a positive role in preventing cervical cancer [ 29 , 30 ]. A key strength of this study is that, despite the methodological challenges of assessing uncommon exposures and outcomes, we were able to evaluate the risk for HPV-associated gynecologic cancers in women of childbearing age with RD. This was made possible by utilizing a large population-based NHIS-NHID database, which contains the heath information of most of the Korean population. The accuracy of the diagnoses was assumed to be reliable, as patients with cancer or rare and intractable diseases required their registration forms to be completed by their physician to receive reduced copayment benefits under the Korean ICBP. These forms included codes for the targeted diseases classified according to the KCD-7 (based on ICD-100, the date of definitive diagnosis, and the tests performed to confirm the diagnosis. A limitation of this study is that we could not assess behavioral characteristics, such as sexual activity, which are known risk factors for HPV infection, as the NHIS-NHID database does not include variables related to behavioral factors. Moreover, this study was unable to directly ascertain the impact of individual drugs or comorbidities on the risk for developing HPV-associated gynecologic cancer. The Korean government offers complimentary biennial cervical cancer screening to all women over the age of 20 years [ 31 ], and offers free HPV vaccines to female adolescents aged 12−17 years and to low-income women aged 18−26 years [ 32 ]. However, the national cancer screening program should be tailored to meet the needs of high-risk populations for gynecologic cancer. Currently, there are no specific recommendations for gynecologic cancer screening and prevention for women with RA and AS. For women with SLE and/or APS, the European League Against Rheumatism recommends a Pap smear examination yearly for heavily immunosuppressed patients or according to the local screening program, if they are considered low-risk patients [ 33 ]. However, regular uptake of cervical cancer screening is reported to be low in Korea (18.9%) [ 34 ]; additionally, the national HPV vaccination initiation rate is low, with a reported rate of only 35.7% [ 35 ]. Studies conducted in Canada and the United States of America have shown that subgroups of women with SLE, typically Caucasian, younger, with a lower education, and with high SLE damage may poorly adhere to preventive health screening programs [ 36 , 37 ]. We need more studies addressing these issues to implement effective health care policies and provide better gynecologic cancer screening and prevention programs for women of childbearing age with RD, as they are a highly vulnerable population for gynecologic cancer. In conclusion, Korean women of childbearing age with RD face an increased risk for HPV-associated gynecologic cancer. This risk may be further increased by comorbidities and medication use during the follow-up period, particularly in patients with SLE. Therefore, it is essential to develop prevention strategies for HPV-associated gynecologic cancer in women with RD of childbearing age, and healthcare providers should put more effort into HPV-associated gynecologic cancer surveillance and education. Conclusion Our study found a threefold increased risk for HPV-associated gynecologic cancer in women of childbearing age with RD compared with those without RD. We provided additional information on the impact of comorbidities and medication use on the risk for HPV-associated gynecologic cancers. demonstrating that these factors influenced the risk in women with SLE, but not in those with seropositive RA or AS. Based on the findings of this study, we suggest that improved screening strategies are needed for HPV-associated gynecologic cancer, particularly considering that women of childbearing age with RD are highly vulnerable population. Abbreviations AS ankylosing spondylitis CI confidence interval HR hazard ratio HPV human papillomavirus IR incidence rate ICBP Individual Copayment Beneficiaries Program KCD Korean Standard Classification of Diseases NHIS-NHID National Health Insurance Service-National Health Information Database NSAID nonsteroidal anti-inflammatory drug OB/GYN obstetrics and gynecology OPD outpatient department Pap Papanicolaou PY person-year RD rheumatic diseases RA rheumatoid arthritis SPRA seropositive rheumatoid arthritis SD standard deviation SLE systemic lupus erythematosus ICD-10 International Classification of Diseases 10th revision Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the National Health Insurance Service Ilsan Hospital, South Korea (NHIMC 2024-01-007). Since the database extracted from the NHIS could not be directly linked to the subjects or identified through any identifiers, our study was exempt from requiring consent to participate. Consent for publication Not applicable Availability of data and materials Not applicable Competing interests The authors declare that they have no competing interests. Funding Not applicable Authors' contributions All authors contributed to the study’s conceptualization and design, the data analysis and interpretation, and the critical revision of the manuscript’s intellectual content. JL wrote the manuscript. JL and JSP have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. HL contributed to data acquisition and statistical analysis. All authors (JL, JSP, HL, IWB, MKC, PGP and CHL) have read and approved the final manuscript. Acknowledgements This work was supported by a grant from the the National Health Insurance Ilsan Hospital, South Korea (NHIMC-2023-PR-002). This study used data from the Korean NHIS-NHID database, created by the NHIS (NHIS-2024-1-395). References Trottier H, Franco EL. The epidemiology of genital human papillomavirus infection. Vaccine. 2006;24(Suppl 1):S1–15. de Martel C, Plummer M, Vignat J, Franceschi S. Worldwide burden of cancer attributable to HPV by site, country and HPV type. Int J Cancer. 2017;141(4):664–70. Appleby P, Beral V, Berrington de González A, Colin D, Franceschi S, Goodill A, Green J, Peto J, Plummer M, Sweetland S. Carcinoma of the cervix and tobacco smoking: collaborative reanalysis of individual data on 13,541 women with carcinoma of the cervix and 23,017 women without carcinoma of the cervix from 23 epidemiological studies. Int J Cancer. 2006;118(6):1481–95. 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Is higher prevalence of cervical intraepithelial neoplasia in women with lupus due to immunosuppression? J Clin Rheumatol. 2010;16(4):153–7. Chung MK, Lee CH, Park JS, Lim HS, Lee J. Incidence and prevalence of seropositive rheumatoid arthritis among Korean women of childbearing age: a nationwide population-based study. Korean J Intern Med. 2023;38(1):125–33. Chung MK, Park JS, Lim H, Lee CH, Lee J. Incidence and prevalence of systemic lupus erythematosus among Korean women in childbearing years: A nationwide population-based study. Lupus. 2021;30(4):674–9. Timur H, Tokmak A, Turkmen GG, Ali Inal H, Uygur D, Danisman N. Pregnancy outcome in patients with ankylosing spondylitis. J Matern Fetal Neonatal Med. 2016;29(15):2470–4. Cheol Seong S, Kim YY, Khang YH, Heon Park J, Kang HJ, Lee H, Do CH, Song JS, Hyon Bang J, Ha S, et al. Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017;46(3):799–800. García-Piñeres AJ, Hildesheim A, Herrero R, Trivett M, Williams M, Atmetlla I, Ramírez M, Villegas M, Schiffman M, Rodríguez AC, et al. Persistent human papillomavirus infection is associated with a generalized decrease in immune responsiveness in older women. Cancer Res. 2006;66(22):11070–6. Chan CK, Aimagambetova G, Ukybassova T, Kongrtay K, Azizan A. Human Papillomavirus Infection and Cervical Cancer: Epidemiology, Screening, and Vaccination-Review of Current Perspectives. J Oncol. 2019;2019:3257939. Chung MK, Lee CH, Park JS, Lim H, Lee J. Burden of comorbidities and medication use in childbearing women with rheumatic diseases: a nationwide population-based study. Korean J Intern Med. 2022;37(6):1250–9. Fanouriakis A, Kostopoulou M, Andersen J, Aringer M, Arnaud L, Bae SC, Boletis J, Bruce IN, Cervera R, Doria A, et al. EULAR recommendations for the management of systemic lupus erythematosus: 2023 update. Ann Rheum Dis. 2024;83(1):15–29. Kim MH, Seo SS, Song YS, Kang DH, Park IA, Kang SB, Lee HP. Expression of cyclooxygenase-1 and – 2 associated with expression of VEGF in primary cervical cancer and at metastatic lymph nodes. Gynecol Oncol. 2003;90(1):83–90. Dai Y, Zhang X, Peng Y, Wang Z. The expression of cyclooxygenase-2, VEGF and PGs in CIN and cervical carcinoma. Gynecol Oncol. 2005;97(1):96–103. Farley JH, Truong V, Goo E, Uyehara C, Belnap C, Larsen WI. A randomized double-blind placebo-controlled phase II trial of the cyclooxygenase-2 inhibitor Celecoxib in the treatment of cervical dysplasia. Gynecol Oncol. 2006;103(2):425–30. Hefler LA, Grimm C, Speiser P, Sliutz G, Reinthaller A. The cyclooxygenase-2 inhibitor rofecoxib (Vioxx) in the treatment of cervical dysplasia grade II-III A phase II trial. Eur J Obstet Gynecol Reprod Biol. 2006;125(2):251–4. Kim Y, Jun JK, Choi KS, Lee HY, Park EC. Overview of the National Cancer screening programme and the cancer screening status in Korea. Asian Pac J Cancer Prev. 2011;12(3):725–30. Kim MA, Han GH, Kim JH, Seo K. Current Status of Human Papillomavirus Infection and Introduction of Vaccination to the National Immunization Program in Korea: an Overview. J Korean Med Sci. 2018;33(52):e331. Andreoli L, Bertsias GK, Agmon-Levin N, Brown S, Cervera R, Costedoat-Chalumeau N, Doria A, Fischer-Betz R, Forger F, Moraes-Fontes MF, et al. EULAR recommendations for women's health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with systemic lupus erythematosus and/or antiphospholipid syndrome. Ann Rheum Dis. 2017;76(3):476–85. Kim JY, Hong J, Yoon J, Park J, Kim TH. Regularity of cervical cancer screening in Korea: analysis using national public data for 12 years. J Gynecol Oncol. 2024;35(2):e18. Ouh YT, Lee JK. Proposal for cervical cancer screening in the era of HPV vaccination. Obstet Gynecol Sci. 2018;61(3):298–308. Bernatsky SR, Cooper GS, Mill C, Ramsey-Goldman R, Clarke AE, Pineau CA. Cancer screening in patients with systemic lupus erythematosus. J Rheumatol. 2006;33(1):45–9. Yazdany J, Tonner C, Trupin L, Panopalis P, Gillis JZ, Hersh AO, Julian LJ, Katz PP, Criswell LA, Yelin EH. Provision of preventive health care in systemic lupus erythematosus: data from a large observational cohort study. Arthritis Res Ther. 2010;12(3):R84. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 19 Aug, 2024 Submission checks completed at journal 19 Aug, 2024 First submitted to journal 09 Aug, 2024 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. 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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-4884521","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342287471,"identity":"57002982-80b4-4543-90bc-3fd39f14f507","order_by":0,"name":"Jisoo Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACPiA2SAAS/AwMbBAhHgJa2GBaJBtI0QIGBgeI1sLefKDgwR8bOePjPWYPfjDYyTPwnH2AXwvPsQSDxLY0Y7MzZ8wNexiSDRt42w3wa5HIMTBIbDicuO1G7jYJHgbmBAZ+Nrw6GNjk338wSPhzOHHz/LfbJP8w1BOhBWiyQQLb4cQNErzbpHkYDicw8LYR0MKTZgD2i8SZ/G/SMgbHDdt4juHXws9++JnhD2CI8bcfS5N8U1Etz8+Thl8LyCKkADJAiik8gPkBEYpGwSgYBaNgJAMAK7U6pExFajwAAAAASUVORK5CYII=","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jisoo","middleName":"","lastName":"Lee","suffix":""},{"id":342287472,"identity":"62247536-b415-467a-b30a-27876c452645","order_by":1,"name":"In-Woon Baek","email":"","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"In-Woon","middleName":"","lastName":"Baek","suffix":""},{"id":342287473,"identity":"5eea164e-5f0d-406b-b085-4bbbe2f3e02e","order_by":2,"name":"Hyunsun Lim","email":"","orcid":"","institution":"National Health Insurance Service Ilsan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hyunsun","middleName":"","lastName":"Lim","suffix":""},{"id":342287474,"identity":"9ec9efe1-c5e6-4155-b39a-9560dcfa64a4","order_by":3,"name":"Min Kyung Chung","email":"","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"Kyung","lastName":"Chung","suffix":""},{"id":342287475,"identity":"cbffe024-c254-4891-82bc-5480829e40b5","order_by":4,"name":"Pil Gyu Park","email":"","orcid":"","institution":"National Health Insurance Service Ilsan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pil","middleName":"Gyu","lastName":"Park","suffix":""},{"id":342287476,"identity":"ff7f783e-ee7b-4770-9017-16c8b47f905d","order_by":5,"name":"Chan Hee Lee","email":"","orcid":"","institution":"Desert Regional Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chan","middleName":"Hee","lastName":"Lee","suffix":""},{"id":342287477,"identity":"dfa80f53-c7e8-44b6-ac8f-b760dc19d133","order_by":6,"name":"Jin Su Park","email":"","orcid":"","institution":"National Health Insurance Service Ilsan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Su","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-08-09 05:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4884521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4884521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64635224,"identity":"e6d95de4-1630-403d-b807-aa8a6ea02de6","added_by":"auto","created_at":"2024-09-16 22:49:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61415,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence rates (IRs) per 100,000 person-years (A) and Kaplan-Meier curves for the cumulative incidence (B) of HPV-associated gynecologic cancer. The RD and non-RD cohorts\u003csup\u003e \u003c/sup\u003eare\u003csup\u003e \u003c/sup\u003eage- and index date-matched. HPV-associated gynecologic cancer included cervical intraepithelial neoplasia grade 3, and cervical, vaginal cancer, and vulva cancers. RD included SLE, SPRA, and AS. RD, rheumatic diseases; SLE, systemic lupus erythematosus; SPRA, seropositive rheumatoid arthritis; AS, ankylosing spondylitis.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4884521/v1/cf78805032bdfa3dfc09d6f9.jpg"},{"id":64635225,"identity":"d1795806-184f-4fc1-95e9-a84f47d1c071","added_by":"auto","created_at":"2024-09-16 22:49:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80565,"visible":true,"origin":"","legend":"\u003cp\u003eHazard ratios (HRs) for HPV-associated gynecologic cancer: multivariate Cox regression models. Model 1 was adjusted for income level, medical insurance type, residential distinct, comorbidities, and preventive medical service utilization at the baseline. Model 2 was adjusted for healthcare and preventive medical service utilization during the follow-up period in addition to the variables in model 1. Model 3 (fully adjusted model) was adjusted for comorbidities and medication use (immunosuppressant use, number of used immunosuppressant, NSAID use) during follow-up in addition t the variables in model 2. The RD and non-RD cohorts\u003csup\u003e \u003c/sup\u003ewere\u003csup\u003e \u003c/sup\u003eage- and index date-matched. HPV-associated gynecologic cancer included cervical intraepithelial neoplasia grade 3, and cervical, vaginal cancer, and vulva cancers. RD included SLE, SPRA, and AS. RD, rheumatic diseases; SLE, systemic lupus erythematosus; SPRA, seropositive rheumatoid arthritis; AS, ankylosing spondylitis.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4884521/v1/23aedf12c30bf4ddc383b026.jpg"},{"id":64635741,"identity":"66108f24-5cd3-46f9-b1dd-0bebd3317b98","added_by":"auto","created_at":"2024-09-16 22:57:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4884521/v1/86b212e3-841f-41c5-be71-060441e6e7ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk for human papillomavirus-associated gynecologic cancer among women of childbearing age with rheumatic diseases: a population-based cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eHuman papillomavirus (HPV) is the most prevalent sexually transmitted infection in the general population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global prevalence rate of genital HPV infection in women is 2\u0026minus;44%, with the highest rates of incident and persistent infections observed in women aged 24\u0026minus;34 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. HPV is a major cause of cervical cancer, which is one of the leading cause of cancer-related deaths among women globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Persistent high-risk HPV infection is a major risk factor for development of HPV-associated gynecologic cancers in women. However, behavioral factors and comorbid conditions, such as risky sexual behaviors, concomitant sexually transmitted infections, tobacco smoking, and immunosuppression also contribute to the increased risk for HPV-associated gynecologic cancer [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmunocompromised women with rheumatic diseases (RD) have been reported to have a higher risk for gynecologic cancer associated with HPV infection. Several studies have demonstrated that women with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) face an increased risk for developing high-grade cervical dysplasia and cervical cancer [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and vaginal and vulva cancers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, immunosuppressive medications used to treat severe SLE, such as cyclophosphamide, mycophenolate mofetil, azathioprine, and high doses of corticosteroids can increase the risk for cervical dysplasia in a cumulative dose- dependent manner, resulting in persistent HPV infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, RD, such as RA, SLE, and ankylosing spondylitis (AS), frequently occur in women of childbearing age [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], however, their risk for HPV-associated gynecologic cancer is not yet fully understood.\u003c/p\u003e \u003cp\u003eThe objective of this study was to evaluate the risk of HPV-associated gynecologic cancer and to determine the impact of comorbidities and medication use on this risk in Korean women with RD during their childbearing years, compared to those without RD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eA cohort study was conducted using data from the Korean National Health Insurance Service-National Health Information Database (NHIS-NHID) between 2009 and 2021. The Korean NHIS is a single insurer that provides coverage for most of the Korean population. The NHIS-NHID is composed of five databases, namely the eligibility, national health screening, health care utilization, long-term care insurance, and health care provider databases, which include information on income-based insurance contributions, residential districts, national health screening, inpatient and outpatient health care utilization (diagnosis and treatment), claims data, and health care providers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor this study, data collected between January 1, 2009 and December 31, 2021 was used. In 2009, the Republic of Korea subsidized medical expenses for patients with rare and intractable diseases through a copayment assistance policy, the Individual Copayment Beneficiaries Program (ICBP); RD, including SLE, seropositive RA (SPRA), and AS were designated as rare diseases covered by this program. Under the ICBP, the NHIS established a registration program that included codes for the targeted diseases classified according to the Korean Standard Classification of Diseases (KCD)-7 (based on the International Classification of Diseases 10\u003csup\u003eth\u003c/sup\u003e revision [ICD-10]), date of definitive diagnosis, and tests performed to confirm the diagnosis. Data from January 1, 2009 was used with the assumption that all patients with SPRA, SLE, and AS were accurately coded, as the ICBP registration required the classification criteria for a definitive diagnosis. This study protocol was approved by the Institutional Review Board of the National Health Insurance Service Ilsan Hospital, South Korea (NHIMC 2024-01-007), and conducted according to the principles of the Declaration of Helsinki. Since the database used in this study contains anonymized data for research purposes, informed consent was not required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy cohort\u003c/h2\u003e \u003cp\u003eWomen of childbearing age (defined as women between the ages of 20 and 49 years), we selected women with and without RD were selected. RD cohort included SLE, SPRA, and AS, identified with the diagnostic codes of M05, M32, and M45 based on the ICD-10 codes in the NHIS-NHID (2011\u0026minus;2021). The women with SLE, SPRA, and AS were included within the RD cohort as subcohorts, as they are representative diseases that occur frequently in women of childbearing age. The start of the follow-up period (i.e. the index date) was defined as the date of the first RD code identification. Women with HPV-associated gynecologic cancer within the 12-months before the index date were excluded. The HPV-associated gynecologic cancer included cervical intraepithelial neoplasia grade 3, and cervical, vaginal, vulva cancers identified with the ICD-10 codes D06, C53, C52, and C519, respectively.\u003c/p\u003e \u003cp\u003eA non-RD cohort of women of childbearing age who did not have RD, including SLE, SPRA and AS in the NHIS-NHID (2011\u0026minus;2021) database, was identified for comparison with the RD cohort. Women in the non-RD cohort were age and index date matched with women in the RD cohort, and were randomly sampled in a 1:5 ratio. In the non-RD cohort, women matched by age and index date with the women with SLE, SPRA, or AS were selected to serve as controls for each RD subcohort.\u003c/p\u003e \u003cp\u003eWomen in the RD and non-RD cohorts were followed up until the earliest occurrence of any of the following events: development of HPV-associated gynecologic cancer, the end of the study database (2021), or death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measures\u003c/h2\u003e \u003cp\u003eThe primary outcome measure was the development of HPV-associated gynecologic cancer. In addition, variables potentially associated with the risk for HPV-associated gynecologic cancer were assessed at the baseline and during the follow-up period. At the baseline, age, income, medical insurance, residential district, comorbidities, smoking status and preventive medical service utilization were assessed. The age, income, medical insurance, and residential district data were obtained at the index date. Comorbidities, including hypertension, diabetes mellitus, hyperlipidemia, and cancer, were identified with the ICD-10 codes I10-12/15, E10-14, E78, and C*, between 1 and 24 months before the index date. Smoking and preventive medical service utilization data were obtained from the NHIS health screening database within 24 months closest to the index date. Variables for the preventive medical service utilization included NHIS health screening and NHIS Papanicolaou (Pap) smear recipients.\u003c/p\u003e \u003cp\u003eDuring the follow-up period, the same comorbidities and preventive medical service utilization data assessed at the baseline were evaluated in addition to the use of medication and health care utilization. Medications included nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, hydroxychloroquine, cyclophosphamide, immunosuppressants, and biologics. Use of immunosuppressants included methotrexate, leflunomide, azathioprine, cyclosporin, tacrolimus, mizoribine, and mycophenolate mofetil. The number of immunosuppressants used was also assessed. Biologics included etanercept, adalimumab, infliximab, golimumab, abatacept, tocilizumab, rituximab, ustekinumab, secukinumab, ixekizumab, tofacitinib, baricitinib, and upadacitinib. Variables included in health care utilization were the total number of outpatient department (OPD) and obstetrics and gynecology (OB/GYN) OPD visits during the follow-up period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA descriptive analysis was performed to summarize the baseline and follow-up characteristics of the cohorts. Categorical variables were presented as frequencies and percentages, and continuous variables were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The baseline and follow-up characteristics of the RD and non-RD cohorts and the RD subcohorts and their controls were compared using the chi-square test for categorical variables and student t-test for continuous variables. The incidence rates (IRs) of HPV-associated gynecologic cancer in the RD and non-RD cohorts and the RD subcohorts were calculated per 100,000 person-years (PYs) with a 95% confidence interval (CI) during the follow-up period. Kaplan\u0026minus;Meier curves were plotted for the cumulative incidence of HPV-associated gynecologic cancer in the RD and non-RD cohorts and the RD subcohorts. Unadjusted and multivariate Cox proportional hazard models, which adjusted for multiple potential confounders associated with HPV infection were used to compare the risk of HPV-associated gynecologic cancer in the RD cohort and subcohorts with the non-Rd or RD subcohort controls, respectively. All statistical analyses were performed using Statistical Analysis Software version 9.4 (SAS Institute Inc., Cary, NC, USA). A p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study cohorts\u003c/h2\u003e \u003cp\u003eThe study included 41,514 women of childbearing age with RD, including 9,932 women with SLE, 23,731 with SPRA, 6,851 with AS, and 199,366 women without RD. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the study cohorts. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD duration of the follow up was similar in the RD and non-RD cohorts, 67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7 and 67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7 months, respectively. Since age was matched, the mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD was similar in the RD (37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8) and non-RD (37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8 years) cohorts; however, mean age was younger in the SLE (33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4) and AS (34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5), and older in the SPRA subcohort (40.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4). In addition, the most common ages in the SLE, SPRA, and AS subcohorts were between 20\u0026minus;24 years, 45\u0026minus;49 years, and 35\u0026minus;39 years, respectively. The RD cohort included more women in the lower income segment and on medical aid compared with the non-RD cohort. Comorbidities and preventive medical service utilization were higher in the RD compared with the non-RD cohort. Smoking status was not significantly different between the RD and non-RD groups.\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\u003eBaseline characteristics of the study cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRD*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPRA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-RD*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40,514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199,366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up period, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026minus;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,874 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,534 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,228 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,112 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,204 (12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026minus;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,820 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,292 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,377 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,151 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18,892 (9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026minus;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,931 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,427 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,357 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,147 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,150 (12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026minus;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,018 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,556 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,235 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,227 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34,429 (17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026minus;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,956 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,649 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,137 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,170 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44,068 (22.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026minus;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,915 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,474 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,397 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,044 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53,623 (26.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level\u003csup\u003e\u0026dagger;,\u003c/sup\u003e segment\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026minus;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,513 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,929 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,665 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,919 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53,570 (26.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026minus;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,389 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,472 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,435 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,482 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72,979 (36.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026minus;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,574(33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,279 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,053 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,242 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67,861 (34.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,050 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,930 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,225 (68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,895 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139,004 (69.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,700 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,439 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,605 (27.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,656 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56,621 (28.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical-aid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,764 (4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e901 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,741 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential district\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity, province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,106 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,137 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,338 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,631 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41,705 (20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,388 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,507 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,199 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,682 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50,906 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,020 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,288 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,194 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,538 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106,755 (53.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003csup\u003e\u0026Dagger;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,605 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,430 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,764 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e411 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,812 (4.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,992 (7.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e815 (8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,724 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e453 (6.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,031 (4.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,598 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.496 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,124 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,978 (28.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,033 (12.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.757 (4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e516 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e967 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,809 (2.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003csup\u003e\u003cb\u003e\u0026sect;\u003c/b\u003e\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 \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\u003e22,604 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,450 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,597 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,557 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108,350 (90.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e958 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,390 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,428 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e841 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e268 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,500 (5.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive medical service utilization\u003csup\u003e\u003cb\u003e\u0026sect;\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHIS health screening recipients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,328 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,579 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,825 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,922 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84,991 (42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHIS Pap smear\u003c/p\u003e \u003cp\u003erecipients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,800 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,014 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,252 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,534 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71,490 (35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCategorial values are provided as n (%) and numerical quantitative data are provided as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003cp\u003eVariables were obtained at the index date unless otherwise indicated.\u003c/p\u003e \u003cp\u003e*RD and non-RD cohorts were age- and index date-matched, RD include SLE, SPRA, and AS.\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003eIncome levels were determined according to the decile method (8\u0026ndash;10 refers to the low-income group)\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026Dagger;\u003c/sup\u003eComorbidities were identified with diagnostic code based on the International Classification of Diseases 10th revision (ICD-10) between 1 and 24months before the index date.\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cb\u003e\u0026sect;\u003c/b\u003e \u003c/sup\u003eSmoking and preventive medical service utilization data were obtained from NHIS Health Screening Examination Database within 24 months closest to the index date.\u003c/p\u003e \u003cp\u003eRD, rheumatic diseases; SLE, systemic lupus erythematosus; SPRA, seropositive rheumatoid arthritis; AS, ankylosing spondylitis; DM, diabetes mellitus; NHIS, National Health Insurance Service; Pap, Papanicolaou.\u003c/p\u003e \u003cp\u003eCharacteristics recorded during the follow-up period are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All comorbidities and medication use were higher in RD compared with the non-RD. The same pattern of comorbidities and medication use was observed in the RD subcohorts compared with their controls, except for cyclophosphamide use in the AS subcohort, which was not significantly different compared with the control. The total number\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of OPD and OB/GYN OPD visits were higher in the RD compared with the non-RD cohort (23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 vs 11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 and 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 vs 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3, respectively) (all p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The number\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of Pap smear received/10 years was also higher in the RD compared with the non-RD cohort (1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12 vs 1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). However, the number\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of NHIS health screening received/10 years was not significantly different between the RD and the non-RD cohorts (2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25 vs 2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30, p\u0026thinsp;=\u0026thinsp;0.927). Among the RD subcohorts, the total number of OPD and OB/GYN OPD visits/year were higher in the all RD subcohorts compared with their controls, with exception of total number of OB/GYN visits/year in SPRA, which was similar compared with the control. The number of NHIS health screening and Pap smear received showed differences among the RD subcohorts. The number of NHIS health screening received/10 years was lower in the SLE and higher in SPRA, but not different in the AS group. In contrast, the number of Pap smear received/10 years was higher in the SPRA and AS subcohorts, but not different in the SLE group compared with their controls.\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\u003eComorbidities, medication, and health care and preventive medical service utilization during the follow-up period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\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\u003eRD*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-RD*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLE*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLE control*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSPRA*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSPRA control*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAS*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAS control*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\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\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40,514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23,731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e116,647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e33,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,662 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,208 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,924 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,136 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,128 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11,164 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e610 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,908 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,308 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,276 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,873 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,394 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,423 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14,058 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,012 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,824 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,016 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,004 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,444 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,921 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12,426 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34,394 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,146 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7,689 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,650 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,733 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,013 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,502 (5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,152 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,621 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e485 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1610 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication use\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,218 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48,182 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,598 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,347 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21,413 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31,564 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6,209 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6,217 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,045 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,383 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,697 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,087 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18,978 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,060 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2,370 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,236 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxychloroquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,904 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,527 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15,196 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e310 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e181 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclophosphamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,166 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e804 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e965 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e176 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e567 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e92 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppressant\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,017 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,135 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,808 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e278 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17,516 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e704 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e693 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of immunosuppressant\u003csup\u003e\u0026dagger;\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 \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \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 \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026minus;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,537 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,108 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,349 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15,512 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e684 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e676 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,480 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e459 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,004 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiologics\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,533 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,381 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2,021 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth care utilization\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo of total OPD\u003c/p\u003e \u003cp\u003evisits/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo of OB/GYN OPD\u003c/p\u003e \u003cp\u003evisits/year,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive medical service utilization\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of NHIS health\u003c/p\u003e \u003cp\u003eScreening received /10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of NHIS Pap smear received/10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCategorial values are provided as n (%) and numerical quantitative data are provided as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003cp\u003e*RD and non-RD, SLE and SLE control, SPRA and SPRA control, and AS and AS control are age- and index date-matched, RD include SLE, SPRA, and AS.\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003eImmunosuppressants include MTX, leflunomide, azathioprine, cyclosporin, tacrolimus, mizoribine, MMF.\u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026Dagger;\u003c/sup\u003eBiologics include etanercept, adlimumab, infliximab, golimumab, abatacept, tocilizumab, rituximab, ustekinumab, secukinumab, ixekinumab, tofacitinib, baricitinib, upadacitinib.\u003c/p\u003e \u003cp\u003eRD, rheumatic diseases; SLE, systemic lupus erythematosus; SPRA, seropositive rheumatoid arthritis; AS, ankylosing spondylitis; NSAIDs nonsteroidal anti-inflammatory drugs, NHIS, National Health Insurance Service; Pap, Papanicolaou.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIncidence rates and cumulative incidence of HPV-associated gynecologic cancer\u003c/h2\u003e \u003cp\u003eThe follow-up period was 67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7 months, during which the IR of HPV-associated gynecologic cancer between the cohorts was 111.5/100,000 PYs in the RD and 73.2/100,000 PYs in the non-RD cohort. Among the RD subcohorts, IR/100,000 PYs of HPV-associated gynecologic cancer was higher in women with SLE (223.6) and SPRA (83.1); in contrast, it was lower in women with AS (69.1) compared with that of the non-RD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Kaplan\u0026minus;Meier curves comparing the cumulative incidence of HPV-associated gynecologic cancer between the RD and non-RD cohorts revealed similar results, showing an increased risk for HPV-associated gynecologic cancer in the RD cohort, with the highest cumulative incidence observed in the SLE group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRisk of HPV-associated gynecologic cancer\u003c/h2\u003e \u003cp\u003eThe unadjusted hazard ratios (HRs) (95% [CI]) for HPV associated gynecologic cancer were raised in the RD (1.83 [1.42\u0026minus;2.6]), SLE (2.00 [1.15\u0026minus;3.45]), and SPRA (2.68 [1.81\u0026minus;3.97]), but not increased in the AS group (0.77 [0.44\u0026minus;1.36]). After full adjustment for possible confounders, the HRs for HPV associated gynecologic cancer were increased in the RD cohort (2.95 [95% CI 2.44\u0026minus;3.57]) and all RD subcohorts (SLE 1.85 [95% CI 1.33\u0026minus;2.57], SPRA 4.10 [95% CI 3.03\u0026minus;5.55], and AS 1.91 [95% CI 1.06\u0026minus;.43]). In the RD subcohorts, the HR was attenuated in the SLE, but increased in the SPRA and AS groups after adjustment for comorbidities and medication use during the follow-up period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to evaluate the risk for HPV-associated gynecologic cancer among women of childbearing age with RD. After adjusting for possible confounders, this study found that Korean women of childbearing age with RD have a threefold increased risk for HPV-associated gynecologic cancer compared with those without RD.\u003c/p\u003e \u003cp\u003eThis study demonstrated that the IR of HPV-associated gynecologic cancer in Korean women of childbearing age with RD was 111.5/100,000 PYs, which is approximately 1.6 times higher than the IR of age-matched women without RD (73.2/100,000 PYs). A direct comparison of the IR of HPV-associated gynecologic cancer between women of childbearing age with RD with RD of all ages is challenging. However, it has been reported that women aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with systemic inflammatory diseases have an IR of 94.2/100,000 PY of high-grade cervical dysplasia and cervical cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; this rate is lower compared with the IR of HPV-associated gynecologic cancer in women of childbearing age with RD found in this study. Additionally, after adjusting for potential confounders such as demographics, preventive medical service utilization, comorbidities, and medication use, this study found that women of childbearing age with RD have a 2.95 times greater risk for developing HPV-associated gynecologic cancer, whereas Kim SC et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported that women aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with SLE and RA had a 1.5 times increased risk of high-grade cervical dysplasia and cervical cancer compared to those without systemic inflammatory diseases. These findings suggest that women of childbearing age with RD seems to be at a higher risk compared with women of all ages with RD.\u003c/p\u003e \u003cp\u003eIn addition, this study found that women of childbearing age with SPRA have the highest risk for HPV-associated gynecologic cancer with an HR of 2.95, followed by those with AS and SLE (HRs of 1.91 and 1.85, respectively). However, Kim SC et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported a higher risk of high-grade cervical dysplasia and cervical cancer in women with SLE (HR 1.53) compared with women with RA (HR 1.49). This discrepancy may be because different age groups included in the two studies. While this study focused solely on women of childbearing age, the study by Kim SC et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] included women across all age groups. Women over 65 years of age are more likely to experience long-term persistence of HPV infection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], increasing their likelihood of developing cervical cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, women with SPRA had the highest mean age among the women of childbearing age, while those with SLE had the lowest mean age, which might have contributed to the increased risk for HPV-associated gynecologic cancer in women with SPRA. However, considering all age groups, the lifetime risk for women with SLE seems to be higher than for women with RA.\u003c/p\u003e \u003cp\u003eThe multivariable analysis examined the impact of medication use and comorbidities on the risk for HPV-associated gynecologic cancer in women with RD of childbearing age; we found that for women with SLE, the use of medications and the presence of associated comorbidities during the follow-up period significantly increased the risk for developing HPV-associated gynecologic cancer. Women of childbearing age with SLE have been reported to have greater burden of comorbidities and immunosuppressive drug use compared with those with SPRA and AS [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This is because SLE is a multisystem disease that necessitates immunosuppressive medication for treating severe organ involvement [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our findings are supported by previous studies showing that women with SLE who are exposed to immunosuppressive have a higher risk of cervical dysplasia, and vaginal and vulva cancers [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, the presence of comorbidities and the use of medications appeared to decrease the risk of HPV-associated gynecologic cancer in women with SPRA and AS. This may be attributed to the widespread use of NSAIDs for managing inflammatory arthritis in patients with RA and AS. The \u003cem\u003eCOX-2\u003c/em\u003e gene has been implicated in early cervical carcinogenesis and tumor progression [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and the result of a few small clinical trials have suggested that COX-2 inhibitors may play a positive role in preventing cervical cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key strength of this study is that, despite the methodological challenges of assessing uncommon exposures and outcomes, we were able to evaluate the risk for HPV-associated gynecologic cancers in women of childbearing age with RD. This was made possible by utilizing a large population-based NHIS-NHID database, which contains the heath information of most of the Korean population. The accuracy of the diagnoses was assumed to be reliable, as patients with cancer or rare and intractable diseases required their registration forms to be completed by their physician to receive reduced copayment benefits under the Korean ICBP. These forms included codes for the targeted diseases classified according to the KCD-7 (based on ICD-100, the date of definitive diagnosis, and the tests performed to confirm the diagnosis. A limitation of this study is that we could not assess behavioral characteristics, such as sexual activity, which are known risk factors for HPV infection, as the NHIS-NHID database does not include variables related to behavioral factors. Moreover, this study was unable to directly ascertain the impact of individual drugs or comorbidities on the risk for developing HPV-associated gynecologic cancer.\u003c/p\u003e \u003cp\u003eThe Korean government offers complimentary biennial cervical cancer screening to all women over the age of 20 years [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and offers free HPV vaccines to female adolescents aged 12\u0026minus;17 years and to low-income women aged 18\u0026minus;26 years [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the national cancer screening program should be tailored to meet the needs of high-risk populations for gynecologic cancer. Currently, there are no specific recommendations for gynecologic cancer screening and prevention for women with RA and AS. For women with SLE and/or APS, the European League Against Rheumatism recommends a Pap smear examination yearly for heavily immunosuppressed patients or according to the local screening program, if they are considered low-risk patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, regular uptake of cervical cancer screening is reported to be low in Korea (18.9%) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; additionally, the national HPV vaccination initiation rate is low, with a reported rate of only 35.7% [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies conducted in Canada and the United States of America have shown that subgroups of women with SLE, typically Caucasian, younger, with a lower education, and with high SLE damage may poorly adhere to preventive health screening programs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We need more studies addressing these issues to implement effective health care policies and provide better gynecologic cancer screening and prevention programs for women of childbearing age with RD, as they are a highly vulnerable population for gynecologic cancer.\u003c/p\u003e \u003cp\u003eIn conclusion, Korean women of childbearing age with RD face an increased risk for HPV-associated gynecologic cancer. This risk may be further increased by comorbidities and medication use during the follow-up period, particularly in patients with SLE. Therefore, it is essential to develop prevention strategies for HPV-associated gynecologic cancer in women with RD of childbearing age, and healthcare providers should put more effort into HPV-associated gynecologic cancer surveillance and education.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study found a threefold increased risk for HPV-associated gynecologic cancer in women of childbearing age with RD compared with those without RD. We provided additional information on the impact of comorbidities and medication use on the risk for HPV-associated gynecologic cancers. demonstrating that these factors influenced the risk in women with SLE, but not in those with seropositive RA or AS. Based on the findings of this study, we suggest that improved screening strategies are needed for HPV-associated gynecologic cancer, particularly considering that women of childbearing age with RD are highly vulnerable population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eankylosing spondylitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman papillomavirus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eincidence rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndividual Copayment Beneficiaries Program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKorean Standard Classification of Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHIS-NHID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health Insurance Service-National Health Information Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSAID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enonsteroidal anti-inflammatory drug\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOB/GYN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eobstetrics and gynecology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoutpatient department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePap\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePapanicolaou\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperson-year\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erheumatic diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erheumatoid arthritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eseropositive rheumatoid arthritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic lupus erythematosus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases 10th revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the National Health Insurance Service Ilsan Hospital, South Korea (NHIMC 2024-01-007). Since the database extracted from the NHIS could not be directly linked to the subjects or identified through any identifiers, our study was exempt from requiring consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study\u0026rsquo;s conceptualization and design, the data analysis and interpretation, and the critical revision of the manuscript\u0026rsquo;s intellectual content. JL wrote the manuscript. JL and JSP have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. HL contributed to data acquisition and statistical analysis. All authors (JL, JSP, HL, IWB, MKC, PGP and CHL) have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the the National Health Insurance Ilsan Hospital, South Korea (NHIMC-2023-PR-002).\u0026nbsp;This study used data from the Korean NHIS-NHID database, created by the NHIS (NHIS-2024-1-395).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrottier H, Franco EL. The epidemiology of genital human papillomavirus infection. Vaccine. 2006;24(Suppl 1):S1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Martel C, Plummer M, Vignat J, Franceschi S. Worldwide burden of cancer attributable to HPV by site, country and HPV type. Int J Cancer. 2017;141(4):664\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppleby P, Beral V, Berrington de Gonz\u0026aacute;lez A, Colin D, Franceschi S, Goodill A, Green J, Peto J, Plummer M, Sweetland S. Carcinoma of the cervix and tobacco smoking: collaborative reanalysis of individual data on 13,541 women with carcinoma of the cervix and 23,017 women without carcinoma of the cervix from 23 epidemiological studies. Int J Cancer. 2006;118(6):1481\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelzle D, Tanaka LF, Lee KK, Ibrahim Khalil A, Baussano I, Shah ASV, McAllister DA, Gottlieb SL, Klug SJ, Winkler AS, et al. Estimates of the global burden of cervical cancer associated with HIV. Lancet Glob Health. 2021;9(2):e161\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Li L, Jiang C, Qin X, Zhou M, Mao X, Xing H. Co-infection with trichomonas vaginalis increases the risk of cervical intraepithelial neoplasia grade 2\u0026ndash;3 among HPV16 positive female: a large population-based study. BMC Infect Dis. 2020;20(1):642.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCervical carcinoma and. sexual behavior: collaborative reanalysis of individual data on 15,461 women with cervical carcinoma and 29,164 women without cervical carcinoma from 21 epidemiological studies. Cancer Epidemiol Biomarkers Prev. 2009;18(4):1060\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden SJ, Doulgeraki T, Bouras E, Markozannes G, Athanasiou A, Grout-Smith H, Kechagias KS, Ellis LB, Zuber V, Chadeau-Hyam M, et al. Risk factors for human papillomavirus infection, cervical intraepithelial neoplasia and cervical cancer: an umbrella review and follow-up Mendelian randomisation studies. BMC Med. 2023;21(1):274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SC, Glynn RJ, Giovannucci E, Hernandez-Diaz S, Liu J, Feldman S, Karlson EW, Schneeweiss S, Solomon DH. Risk of high-grade cervical dysplasia and cervical cancer in women with systemic inflammatory diseases: a population-based cohort study. Ann Rheum Dis. 2015;74(7):1360\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojo Contreras W, Montoya Fuentes H, Gamez Nava JI, Suarez Rincon AE, Vazquez Salcedo J, Padilla Rosas M, Baltazar Rodriguez LM, Trujillo X, Ramirez Flores M, Trujillo Hernandez B, et al. [Prevalence and cervical human papilloma virus associated factors in patients with rheumatoid arthritis]. Ginecol Obstet Mex. 2008;76(1):9\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojo-Contreras W, Olivas-Flores EM, Gamez-Nava JI, Montoya-Fuentes H, Trujillo-Hernandez B, Trujillo X, Suarez-Rincon AE, Baltazar-Rodriguez LM, Sanchez-Hernandez J, Ramirez-Flores M, et al. Cervical human papillomavirus infection in Mexican women with systemic lupus erythematosus or rheumatoid arthritis. Lupus. 2012;21(4):365\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantana IU, Gomes Ado N, Lyrio LD, Rios Grassi MF, Santiago MB. Systemic lupus erythematosus, human papillomavirus infection, cervical pre-malignant and malignant lesions: a systematic review. Clin Rheumatol. 2011;30(5):665\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTam LS, Chan PK, Ho SC, Yu MY, Yim SF, Cheung TH, Wong MC, Cheung JL, Li EK. Risk factors for squamous intraepithelial lesions in systemic lupus erythematosus: a prospective cohort study. Arthritis Care Res (Hoboken). 2011;63(2):269\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZard E, Arnaud L, Mathian A, Chakhtoura Z, Hie M, Touraine P, Heard I, Amoura Z. Increased risk of high grade cervical squamous intraepithelial lesions in systemic lupus erythematosus: A meta-analysis of the literature. Autoimmun Rev. 2014;13(7):730\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDreyer L, Faurschou M, Mogensen M, Jacobsen S. High incidence of potentially virus-induced malignancies in systemic lupus erythematosus: a long-term followup study in a Danish cohort. Arthritis Rheum. 2011;63(10):3032\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBateman H, Yazici Y, Leff L, Peterson M, Paget SA. Increased cervical dysplasia in intravenous cyclophosphamide-treated patients with SLE: a preliminary study. Lupus. 2000;9(7):542\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernatsky S, Ramsey-Goldman R, Gordon C, Joseph L, Boivin JF, Rajan R, Allen A, Moore AD, Leung MH, Clarke A. Factors associated with abnormal Pap results in systemic lupus erythematosus. Rheumatology (Oxford). 2004;43(11):1386\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgnenovski VM, Marder W, Somers EC, Johnston CM, Farrehi JG, Selvaggi SM, McCune WJ. Increased incidence of cervical intraepithelial neoplasia in women with systemic lupus erythematosus treated with intravenous cyclophosphamide. J Rheumatol. 2004;31(9):1763\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlumb EM, Ara\u0026uacute;jo ML Jr., Jesus GR, Santos DB, Oliveira AV, Albuquerque EM, Macedo JM. Is higher prevalence of cervical intraepithelial neoplasia in women with lupus due to immunosuppression? J Clin Rheumatol. 2010;16(4):153\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung MK, Lee CH, Park JS, Lim HS, Lee J. Incidence and prevalence of seropositive rheumatoid arthritis among Korean women of childbearing age: a nationwide population-based study. Korean J Intern Med. 2023;38(1):125\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung MK, Park JS, Lim H, Lee CH, Lee J. Incidence and prevalence of systemic lupus erythematosus among Korean women in childbearing years: A nationwide population-based study. Lupus. 2021;30(4):674\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimur H, Tokmak A, Turkmen GG, Ali Inal H, Uygur D, Danisman N. Pregnancy outcome in patients with ankylosing spondylitis. J Matern Fetal Neonatal Med. 2016;29(15):2470\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheol Seong S, Kim YY, Khang YH, Heon Park J, Kang HJ, Lee H, Do CH, Song JS, Hyon Bang J, Ha S, et al. Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017;46(3):799\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Pi\u0026ntilde;eres AJ, Hildesheim A, Herrero R, Trivett M, Williams M, Atmetlla I, Ram\u0026iacute;rez M, Villegas M, Schiffman M, Rodr\u0026iacute;guez AC, et al. Persistent human papillomavirus infection is associated with a generalized decrease in immune responsiveness in older women. Cancer Res. 2006;66(22):11070\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan CK, Aimagambetova G, Ukybassova T, Kongrtay K, Azizan A. Human Papillomavirus Infection and Cervical Cancer: Epidemiology, Screening, and Vaccination-Review of Current Perspectives. J Oncol. 2019;2019:3257939.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung MK, Lee CH, Park JS, Lim H, Lee J. Burden of comorbidities and medication use in childbearing women with rheumatic diseases: a nationwide population-based study. Korean J Intern Med. 2022;37(6):1250\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFanouriakis A, Kostopoulou M, Andersen J, Aringer M, Arnaud L, Bae SC, Boletis J, Bruce IN, Cervera R, Doria A, et al. EULAR recommendations for the management of systemic lupus erythematosus: 2023 update. Ann Rheum Dis. 2024;83(1):15\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim MH, Seo SS, Song YS, Kang DH, Park IA, Kang SB, Lee HP. Expression of cyclooxygenase-1 and \u0026ndash;\u0026thinsp;2 associated with expression of VEGF in primary cervical cancer and at metastatic lymph nodes. Gynecol Oncol. 2003;90(1):83\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai Y, Zhang X, Peng Y, Wang Z. The expression of cyclooxygenase-2, VEGF and PGs in CIN and cervical carcinoma. Gynecol Oncol. 2005;97(1):96\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarley JH, Truong V, Goo E, Uyehara C, Belnap C, Larsen WI. A randomized double-blind placebo-controlled phase II trial of the cyclooxygenase-2 inhibitor Celecoxib in the treatment of cervical dysplasia. Gynecol Oncol. 2006;103(2):425\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHefler LA, Grimm C, Speiser P, Sliutz G, Reinthaller A. The cyclooxygenase-2 inhibitor rofecoxib (Vioxx) in the treatment of cervical dysplasia grade II-III A phase II trial. Eur J Obstet Gynecol Reprod Biol. 2006;125(2):251\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim Y, Jun JK, Choi KS, Lee HY, Park EC. Overview of the National Cancer screening programme and the cancer screening status in Korea. Asian Pac J Cancer Prev. 2011;12(3):725\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim MA, Han GH, Kim JH, Seo K. Current Status of Human Papillomavirus Infection and Introduction of Vaccination to the National Immunization Program in Korea: an Overview. J Korean Med Sci. 2018;33(52):e331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreoli L, Bertsias GK, Agmon-Levin N, Brown S, Cervera R, Costedoat-Chalumeau N, Doria A, Fischer-Betz R, Forger F, Moraes-Fontes MF, et al. EULAR recommendations for women's health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with systemic lupus erythematosus and/or antiphospholipid syndrome. Ann Rheum Dis. 2017;76(3):476\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, Hong J, Yoon J, Park J, Kim TH. Regularity of cervical cancer screening in Korea: analysis using national public data for 12 years. J Gynecol Oncol. 2024;35(2):e18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuh YT, Lee JK. Proposal for cervical cancer screening in the era of HPV vaccination. Obstet Gynecol Sci. 2018;61(3):298\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernatsky SR, Cooper GS, Mill C, Ramsey-Goldman R, Clarke AE, Pineau CA. Cancer screening in patients with systemic lupus erythematosus. J Rheumatol. 2006;33(1):45\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazdany J, Tonner C, Trupin L, Panopalis P, Gillis JZ, Hersh AO, Julian LJ, Katz PP, Criswell LA, Yelin EH. Provision of preventive health care in systemic lupus erythematosus: data from a large observational cohort study. Arthritis Res Ther. 2010;12(3):R84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rheumatic diseases, Human papillomavirus, gynecologic cancer, Childbearing age, Hazard ratio","lastPublishedDoi":"10.21203/rs.3.rs-4884521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4884521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvaluate the risk of human papillomavirus (HPV)-associated gynecologic cancer in women with rheumatic diseases (RD) during their childbearing years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing Korean National Health Insurance Service-National Health Information Database data (2011\u0026minus;2021), we conducted a cohort study of 40,514 women with RD and 199,366 women without RD aged 20\u0026ndash;49 years. The RD cohort included 9,932 with systemic lupus erythematosus (SLE), 23,731 with seropositive rheumatoid arthritis (SPRA), and 6,851 with ankylosing spondylitis (AS). Incidence rates and hazard ratios for HPV-associated gynecologic cancer, including cervical intraepithelial neoplasia grade 3, and cervical, vaginal, and vulva cancers, were estimated using Cox regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver the mean (standard deviation) follow-up period of 67.5 (37.7) months, the incidence rate of HPV-associated gynecologic cancer was 111.5/100,000 person-years in the RD cohort and 73.2/100,000 person-years in the non-RD cohort. Among the RD subcohorts, the incidence rate/100,000 person-years of HPV-associated gynecologic cancer were higher in SLE (223.6) and SPRA (83.1), and lower in AS (69.1) compared with non-RD. The fully adjusted hazard ratio for HPV-associated gynecologic cancer was higher in the RD cohort (2.95 [95% CI 2.44\u0026ndash;3.57]) and all the RD subcohorts (SLE 1.85 [95% CI 1.33\u0026ndash;2.57], SPRA 4.10 [95% CI 3.03\u0026ndash;5.55] and AS 1.91 [95% CI 1.06\u0026ndash;3.43]). After adjusting for comorbidities and medication use, hazard ratios increased in SPRA and AS but decreased in SLE.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eKorean women of childbearing age with RD have a threefold increased risk for HPV-associated gynecologic cancer compared with those without RD. The risk may be influenced by comorbidities and medication use in SLE. Improved screening strategies are needed for these women.\u003c/p\u003e","manuscriptTitle":"Risk for human papillomavirus-associated gynecologic cancer among women of childbearing age with rheumatic diseases: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 22:49:02","doi":"10.21203/rs.3.rs-4884521/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-08-19T17:50:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-19T04:23:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Arthritis Research \u0026 Therapy","date":"2024-08-09T05:36:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec81bfd1-5b3d-4c2f-9558-25a62cf848ca","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T22:49:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 22:49:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4884521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4884521","identity":"rs-4884521","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}