Analysis of Diagnosis Rate and Factors Influencing the Implementation of Confirmatory Tests in Women with Epithelial Abnormalities of Squamous Cells in Pap Tests: 5 years follow-up using the National Health Insurance Service database.
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Objective: this study aims to investigate the diagnosis rates of cervical cancer and carcinoma in situ between women who did not undergo confirmatory tests within 6 months and those who did, considering influencing factors. Methods: : We identified all Korean women who participate in nationwide cervical cancer screening program from January 2011 and December 2017 using the National Health Insurance Service (NHIS) database. Results: : Among the 21,452,058 eligible patients from 28,619,210 Pap smear tests (2011-2017), 450,349 (1.57%) had squamous cell abnormalities in Pap smear results. Among women notified of squamous cell abnormalities, 124,135 (27.6%) underwent a confirmatory test within 6 months. Among these, there were 4,529 women (3.65%) diagnosed with cervical cancer (C53) and 11,465 women (9.24%) diagnosed with carcinoma in situ (D06). Women who did not undergo confirmatory tests within 6 months were 326,214 (72.4%). Among them, within 5 years, 5,198 women (1.59%) were diagnosed with cervical cancer (C53), and 9,517 women (9.24%) were diagnosed with carcinoma in situ (D06). For cervical cancer (RR 4.11, 95% CI: 3.72-4.54), the diagnosis rate was significantly higher in women over 70. Both cervical cancer (RR 0.73, 95% CI: 0.66-0.79) and carcinoma in situ (RR 0.85, 95% CI: 0.81-0.90) showed significantly lower diagnosis rates in highest economic status. Conclusions: : Encouraging confirmatory tests for abnormal Pap smears is crucial, particularly among the elderly and those with lower economic status.
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Analysis of Diagnosis Rate and Factors Influencing the Implementation of Confirmatory Tests in Women with Epithelial Abnormalities of Squamous Cells in Pap Tests: 5 years follow-up using the National Health Insurance Service database. | 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 Analysis of Diagnosis Rate and Factors Influencing the Implementation of Confirmatory Tests in Women with Epithelial Abnormalities of Squamous Cells in Pap Tests: 5 years follow-up using the National Health Insurance Service database. Hyeongsu Kim, Jong Ha Hwang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3856728/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: this study aims to investigate the diagnosis rates of cervical cancer and carcinoma in situ between women who did not undergo confirmatory tests within 6 months and those who did, considering influencing factors. Methods: We identified all Korean women who participate in nationwide cervical cancer screening program from January 2011 and December 2017 using the National Health Insurance Service (NHIS) database. Results: Among the 21,452,058 eligible patients from 28,619,210 Pap smear tests (2011-2017), 450,349 (1.57%) had squamous cell abnormalities in Pap smear results. Among women notified of squamous cell abnormalities, 124,135 (27.6%) underwent a confirmatory test within 6 months. Among these, there were 4,529 women (3.65%) diagnosed with cervical cancer (C53) and 11,465 women (9.24%) diagnosed with carcinoma in situ (D06). Women who did not undergo confirmatory tests within 6 months were 326,214 (72.4%). Among them, within 5 years, 5,198 women (1.59%) were diagnosed with cervical cancer (C53), and 9,517 women (9.24%) were diagnosed with carcinoma in situ (D06). For cervical cancer (RR 4.11, 95% CI: 3.72-4.54), the diagnosis rate was significantly higher in women over 70. Both cervical cancer (RR 0.73, 95% CI: 0.66-0.79) and carcinoma in situ (RR 0.85, 95% CI: 0.81-0.90) showed significantly lower diagnosis rates in highest economic status. Conclusions: Encouraging confirmatory tests for abnormal Pap smears is crucial, particularly among the elderly and those with lower economic status. Cervical cancer Carcinoma in situ squamous cell abnormalities Korean nationwide cervical cancer screening program Figures Figure 1 Introduction Cervical cancer, a significant global health concern, necessitates comprehensive screening programs for early detection and effective management. The World Health Organization (WHO) has adopted a cervical cancer elimination strategy as a public health initiative, aiming to achieve the 90-70-90 targets by 2030. ( 1 ) Confirmatory tests following abnormal Pap results are crucial in this context, providing a gateway to precise diagnosis and timely intervention. When notified of an abnormal Pap smear result, approximately 53–75% of the general population undergoes appropriate follow-up for confirmatory tests. ( 2 – 5 ) The decision for confirmatory testing is influenced by various factors, including the severity of the Pap test, age, smoking, race, type of insurance, distance to the hospital, and more. ( 2 – 5 ) Understanding how these factors interact is crucial to enhancing the effectiveness of cervical cancer screening programs. Existing studies have predominantly been conducted in Western countries, often centered around single institutions or hospitals, making it challenging to find nationwide studies using big data from national screening programs. This study is based on South Korea's National Health Insurance Service database. The study analyzed variables influencing the performance of confirmatory tests for cervical cancer following the notification of an abnormal Pap smear result. The study also divides participants into groups based on whether they underwent confirmatory tests and tracks them for five years. By comparing the diagnosis rates of cervical cancer and carcinoma in situ between the two groups over time, the study aims to identify differences in diagnosis rates and contribute insights to improve existing cervical cancer screening strategies for better health outcomes. Patients and Methods 1. Study design and data source We obtained data of Papanicolaou (Pap) smear, which is the screening test for uterine cervical cytology, from the National Health Insurance Service (NIHS) data. The health insurance data filed for medical claim was extracted from the NIHS database (Approval no. NHIS-2023-1-614). South Korea Government provide free Pap smear screenings every two years for all Korean women aged 20 and above since 2015. ( 6 ) The conventional Pap smear was used for uterine cervical cancer screening. We excluded liquid based cervical cytology to screen uterine cervical cancer that is not covered by Korean medical insurance. 2. Study population All South Korean women, who were diagnosed with abnormal pap smear, were identified from the NHIS database between January 2011 and December 2017. Women over 30 who participated in population-based cervical cancer screening programs were included. The Korean government implements a cervical cancer screening program on a biennial basis. Eligibility for screening is determined by the birth year of women, with those born in odd-numbered years eligible for screening in odd-numbered years and those born in even-numbered years eligible for screening in even-numbered years. In cases where women do not undergo screening in the designated year, they have the option to apply for an extension and receive the screening the following year. However, for the purpose of this study, women who applied for an extension and underwent the screening in the subsequent year were excluded from the analysis. Disease codes (C53: uterine cervical cancer, D06: carcinoma in situ, N87: cervical dysplasia, C54: endometrial cancer, C56: ovarian cancer) were used to exclude women who have received a diagnosis of gynecologic cancer and cervical dysplasia within 5 years from the date of Pap smear. Disease codes were standardized according to the Korea Standard Classification of Disease-6 (KCD-6), which is a modification of the International Classification of Disease and Related Health Problems, 10th edition (ICD-10), suited to Korean medical circumstances. The patients who underwent total hysterectomy were not included. Korea Health Insurance Review & Assessment Service (HIRA) released procedure and operation codes annually. The women without uterus were identified using operation codes (R0141, R0142, R4147, R4148, R4149, and R4140) including hysterectomy. Pap test outcomes underwent categorization according to the pathological findings delineated by the 2001 Bethesda system.( 7 ) Normal Pap test outcomes were construed as those denoted as negative for intraepithelial lesions (NIL) or reactive cellular change (RCC). Abnormal Pap results pertaining to squamous cell manifestations were construed as those identified as atypical squamous cell of undetermined significance (ASCUS), atypical squamous cell with inconclusive exclusion of high-grade squamous intraepithelial lesion (ASC-H), Low-grade squamous intraepithelial lesion (LSIL), High-grade squamous cell intraepithelial lesion (HSIL), and Squamous cell carcinoma (SCC). Patient attributes encompassed their age during the screening examination, financial standing ascertained through insurance categorization, the presence of enduring ailments, and their residency. Age was treated as a categorical parameter and compartmentalized into the subsequent brackets: 30–39, 40–49, 50–59, 60–69, and 70 years or more. In South Korea, health insurance premiums are computed by the government predicated on an individual's financial capacity. Economic status was stratified into four cohorts contingent on the spectrum of health insurance premium disbursements: 0–4 range, 5–8 range, 9–12 range, 13–16 range, and 17–20 range, with a higher numeral indicative of a superior economic standing. Residency was sorted into clusters comprising Seoul, metropolitan city, city, and county. 3. Identification of confirmatory test in patients who have abnormal glandular Pap results and follow-up Confirmatory examinations comprised human papillomavirus (HPV) testing [PCR-based: D6586, C6033, DNA chip: C6031, D6592], cervical punch biopsy [C8570], and colposcopy [E7722: colposcopy with endocervical speculum, E7721: colposcopy without endocervical speculum]. In cases where Pap results indicated ASC-US, fulfillment of at least one of the ensuing assessments—cervical punch biopsy, colposcopy, or HPV test—was operationally construed as conducting a confirmatory test. For Pap results denoting ASC-H, LSIL, HSIL, and SCC, undertaking a cervical punch biopsy or colposcopy was operationally construed as conducting a confirmatory test. The computation sought to ascertain the count of women subjected to a confirmatory test within six months from the communication of abnormal Pap smear findings. Furthermore, scrutiny extended to discerning the presence of cervical pathology within 6 to 60 months among individuals with abnormal Pap smear results who refrained from undergoing a confirmatory test within the initial 6 months. 4. Statistical analysis The abnormal Pap rate, implementation rate of confirmatory test following abnormal Pap smears, and the percentage ratio of women diagnosed with uterine cervical cancer (C56) in comparison to carcinoma in situ (D06) were systematically documented and subjected to statistical scrutiny utilizing Microsoft Excel 365. The relative risk of uterine cervical cancer (C56) and carcinoma in situ (D06) diagnoses was computed for each age group (30–39 years), economic stratum (0–4), and urbanization level (Seoul) as the baseline, employing the analytical tools provided by MedCalc®. 5. Ethics statement The Institutional Review Board of Konkuk University School of Medicine, Seoul, Republic of Korea (7001355-202210-E-179) reviewed and approved this study. Informed consent was not required from the subjects as the study utilized publicly available secondary data from NHIS. Results Figure 1 presents the flowchart of the study. A total of 28,619,210 Pap smear tests were conducted between 2011 and 2017. Among the 21,452,058 eligible patients who met the predetermined criteria, 450,349 (1.57%) had squamous cell abnormalities in Pap smear results. Among women notified of squamous cell abnormalities, 124,135 (27.6%) underwent a confirmatory test within 6 months. Among these, there were 4,529 women (3.65%) diagnosed with cervical cancer (C53), and 11,465 women (9.24%) diagnosed with carcinoma in situ (D06). The number of women who did not undergo confirmatory tests within 6 months was 326,214 (72.4%). Among them, within 5 years, 5,198 women (1.59%) were diagnosed with cervical cancer (C53), and 9,517 women (9.24%) were diagnosed with carcinoma in situ (D06). Table 1 displays the rates of confirmatory tests based on age, economic status, and residential area for women with abnormal Pap results. The participation rate for confirmatory tests was highest in the 30s, reaching 32.7%, and decreased with increasing age, with the lowest rate observed in those aged 70 and above at 21.4%. Across all age groups, there was an increasing trend in the participation rate for confirmatory tests over the study period, but the increment was the smallest in the 70 and above age group, with only 11.9%. Examining the differences based on economic status, the participation rate was highest in the middle-income bracket (level 9–12), although the variation compared to other income brackets was not substantial, ranging from 0.3–1.3%. Regarding residential differences, women residing in Metropolitan areas had the highest participation rate at 29.9%, while those in county areas had the lowest at 25.6%. Throughout the study period, the increase in participation rate was most pronounced among women residing in county areas at 15.5%. Tables 2 and 3 illustrates the cervical cancer diagnosis rates and relative risk based on the participation in confirmatory test, age, economic status, and degree of urbanization. Among women who underwent confirmatory test within 6 months after notification of abnormal Pap results, the rate of cervical cancer diagnosis was lowest in women in their 30s at 2.37%, with 732 cases. As age increased, the diagnosis rate also increased, reaching the highest at 9.38% (710 cases) in women aged 70 and above (Relative risk 4.11, 95% CI: 3.72–4.54). The diagnosis rate of cervical cancer was higher in groups with lower economic status and in areas with lower urbanization. When examining the rate of cervical cancer diagnosed within 5 years after 6 months of non-participation in confirmatory test, it was lowest in women in their 30s at 1.09%, and highest in women in their 70s at 2.91%. Comparing the rate of diagnosis after 6 months with participation in precision examinations to the rate after 6 months without participation, women in their 70s had the lowest at 0.31%. Examining the rate of cervical cancer diagnoses within 5 years among women who did not undergo a confirmatory test within 6 months, the rate was lowest in women in their 30s at 1.09% and highest in women in their 70s at 2.91%. The percentage ratio of patients diagnosed with cervical cancer after 6 months compared to those diagnosed within 6 months of an abnormal Pap smear was lowest in women in their 70s at 0.31%. Table 1. The implementation rate of a confirmatory test in patients with abnormal Pap smear, N (% a ). Characteristics 2011 2012 2013 2014 2015 2016 2017 Total Age b (years old) 30–39 1,640(25.3) 2,160(27.1) 3,068(25.6) 3,923(30.3) 5,716(33.12) 6,468(36.8) 7,898(38.9) 30891 (32.7) 40–49 3,109(22.0) 3,163(22.3) 4,233(21.8) 5,084(25.8) 6,984(28.65) 7,256(32.4) 8,773(34.8) 38602 (27.7) 50–59 2,385(20.0) 2,391(19.9) 3,120(19.6 3,881(23.5) 5,644(27.19) 5,695(30.6) 7,584(34.2) 30700 (26.0) 60–69 1,153(18.6) 1,232(19.4) 1,431(18.6) 1,885(21.4) 3,051(27.46) 3,264(31.7) 4,360(34.4) 16376 (26.0) Over 70 578(16.6) 607(16.1) 729(16.4) 949(18.8) 1,286(22.09) 1,468(24.8) 1,949(28.5) 7566 (21.4) Economic status 0–4 1,842(20.3) 1,849(20.9) 2,703(20.6) 3,077(23.7) 4,863(28.39) 4,769(31.9) 6,294(34.1) 25397 (26.9) 5–8 1,500(20.7) 1,572(21.2) 2,092(21.4) 2,723(25.1) 3,861(29.01) 4,383(32.8) 5,473(35.3) 21604 (27.9) 9–12 1,552(21.0) 1,723(22.7) 2,224(21.1) 2,906(26.1) 4,214(28.78) 4,551(32.6) 5,870(35.5) 23040 (28.2) 13–16 1,728(21.6) 1,891(21.5) 2,505(21.3) 3,189(25.3) 4,523(28.73) 4,825(32.6) 6,054(35.5) 24715 (27.8) 17–20 2,055(21.4) 2,235(21.9) 2,734(21.4) 3,524(24.9) 4,838(28.34) 5,151(31.5) 6,329(35.0) 26866 (27.4) Residence Seoul 1,897(20.1) 2,061(19.8) 2,850(20.2) 3,523(23.8) 4,760(25.8) 4,834(29.4) 6,265(34.7) 26190 (25.8) Metropolitan 2,287(21.9) 2,584(24.3) 3,284(22.8) 4,121(27.48) 5,973(31.8) 6,325(36.1) 7,639(36.5) 32213 (29.9) City 4,089(21.5) 4,267(21.4) 5,648(21.0) 7,076(24.49) 10,520(28.53) 11,444(32.0) 14,753(34.8) 57797 (27.5) County 565(17.5) 617(19.7) 793(20.0) 975(23.52) 1,399(27.31) 1,530(30.6) 1,889(33.0) 7768 (25.6) a: (The number of women who underwent a confirmatory test within 6 months after Pap smear/The number of women who underwent a Pap smear in the current year) * 100 b: The age at which the Pap smear was conducted. Table 2 The number of women diagnosed with uterine cervical cancer (C56), N (%). Characteristics Within 6 months a Until 2nd year b Until 3rd year b Until 4th year b Until 5th year b Ratio c Age d (years old) 30–39 732 (2.37) 368 (0.58) 513 (0.81) 605 (0.95) 694 (1.09) 0.46 40–49 1,237 (3.20) 824 (0.82) 1,111 (1.10) 1,273 (1.26) 1,469 (1.46) 0.46 50–59 1,032 (3.36) 751 (0.86) 968 (1.11) 1,135 (1.30) 1,278 (1.47) 0.44 60–69 818 (5.00) 567 (1.21) 728 (1.56) 837 (1.79) 950 (2.03) 0.41 Over 70 710 (9.38) 473 (1.71) 602 (2.17) 702 (2.53) 807 (2.91) 0.31 Economic level 0–4 1,103 (4.34) 746 (1.08) 996 (1.44) 1,151 (1.66) 1,320 (1.91) 0.44 5–8 812 (3.76) 529 (0.95) 699 (1.25) 831 (1.49) 935 (1.67) 0.39 9–12 891 (3.87) 563 (0.96) 725 (1.23) 839 (1.43) 966 (1.64) 0.42 13–16 790 (3.20) 547 (0.85) 727 (1.13) 834 (1.30) 975 (1.52) 0.48 17–20 845 (3.15) 546 (0.77) 700 (0.98) 810 (1.14) 904 (1.27) 0.40 Level of Urbanization Seoul 839 (3.20) 537 (0.71) 712 (0.94) 822 (1.09) 925 (1.23) 0.38 Metropolitan 1,208 (3.75) 832 (1.10) 1,093 (1.45) 1,273 (1.69) 1,445 (1.91) 0.51 City 2,092 (3.62) 1,344 (0.88) 1,764 (1.16) 2,035 (1.34) 2,349 (1.55) 0.43 County 384 (4.94) 264 (1.17) 346 (1.53) 414 (1.83) 471 (2.09) 0.42 a: (The number of women diagnosed with uterine cervical cancer (C56)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100 b: (The cumulative number of women diagnosed with uterine cervical cancer (C56)/The number of women who did not undergo a confirmatory test within 6 months after abnormal Pap smear) * 100 c: Percent to ratio of patients diagnosed with cervical cancer after 6 months compared to those diagnosed within 6 months of an abnormal Pap smear. d: The age at which the Pap smear was conducted. Table 3. The relative risk of being diagnosed with uterine cervical cancer (C56) Characteristics Within 6 months a Until 2nd year b Until 3rd year b Until 4th year b Until 5th year b Age c (years old) 30–39 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 40–49 1.35(1.24–1.48) 1.42(1.25–1.60) 1.37(1.23–1.52) 1.33(1.21–1.46) 1.34(1.22–1.46) 50–59 1.42(1.29–1.56) 1.49(1.32–1.69) 1.37(1.24–1.53) 1.37(1.24–1.51) 1.35(1.23–1.47) 60–69 2.11(1.91–2.32) 2.10(1.84–2.39) 1.94(1.73–2.17) 1.89(1.70–2.09) 1.87(1.69–2.06) Over 70 4.11(3.72–4.54) 2.95(2.58–3.38) 2.70(2.40–3.03) 2.67(2.39–2.97) 2.67(2.42–2.95) Economic level 0–4 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 5–8 0.87(0.79–0.95) 0.88(0.77–0.98) 0.88(0.80–0.97) 0.89(0.82–0.98) 0.88(0.81–0.95) 9–12 0.89(0.82–0.97) 0.89(0.80–0.99) 0.87(0.79–0.96) 0.86(0.79–0.94) 0.86(0.79–0.93) 13–16 0.74(0.67–0.81) 0.79(0.71–0.88) 0.80(0.73–0.88) 0.78(0.72–0.85) 0.80(0.73–0.87) 17–20 0.73(0.66–0.79) 0.71(0.64–0.79) 0.69(0.63–0.76) 0.68(0.63–0.75) 0.67(0.61–0.72) Level of Urbanization Seoul 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) Metropolitan 1.17(1.07–1.28) 1.55(1.39–1.72) 1.53(1.40–1.68) 1.55(1.42–1.69) 1.56(1.44–1.69) City 1.13(1.04–1.22) 1.24(1.12–1.37) 1.23(1.13–1.34) 1.23(1.13–1.33) 1.26(1.17–1.36) County 1.54(1.37–1.74) 1.64(1.42–1.90) 1.63(1.43–1.85) 1.68(1.50–1.89) 1.70(1.53–1.90) a: (The number of women diagnosed with uterine cervical cancer (C56)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100 b: (The cumulative number of women diagnosed with uterine cervical cancer (C56)/The number of women who did not undergo a confirmatory test within 6 months after abnormal Pap smear) * 100 c: The age at which the Pap smear was conducted. Tables 4 and 5 depict the rates of carcinoma in situ diagnoses and the relative risk associated with confirmatory test participation, age, economic status, and degree of urbanization. Among women who participated in confirmatory test within 6 months after receiving abnormal Pap results, the incidence of carcinoma in situ was highest in women in their 70s at 11.58% (876 cases), followed by women in their 30s at 11.23% (3,469 cases). There was no statistically significant difference between women in their 30s and 70s (RR 1.03, 95% CI: 0.96–1.11). Notably, the group with the highest economic status ( 17 – 20 ) exhibited a significantly lower diagnostic rate at 8.06% (2,162 cases) (RR 0.85, 95% CI: 0.81–0.90). In areas with lower urbanization, specifically in counties, the diagnosis rate of carcinoma in situ was significantly higher at 10.27% (798 cases) (RR 1.13, 95% CI: 1.05–1.22). Table 4 The number of women diagnosed with carcinoma in situ (D06), N (%). Characteristics Within 6 months a Until 2nd year b Until 3rd year b Until 4th year b Until 5th year b Ratio c Age d (years old) 30–39 3,469 (11.23) 1,453 (2.28) 1,862 (2.92) 2,126 (3.34) 2,433 (3.82) 0.34 40–49 3,764 (9.75) 1,902 (1.89) 2,474 (2.45) 2,816 (2.79) 3,166 (3.14) 0.32 50–59 1,966 (6.40) 1,042 (1.19) 1,375 (1.58) 1,570 (1.80) 1,817 (2.08) 0.33 60–69 1,390 (8.49) 764 (1.64) 1,017 (2.18) 1,166 (2.50) 1,363 (2.92) 0.34 Over 70 876 (11.58) 401 (1.45) 565 (2.04) 628 (2.26) 738 (2.66) 0.23 Economic level 0–4 2,503 (9.86) 1,213 (1.75) 1,563 (2.26) 1,803 (2.61) 2,069 (2.99) 0.30 5–8 2,075 (9.60) 953 (1.71) 1,305 (2.33) 1,475 (2.64) 1,710 (3.06) 0.32 9–12 2,209 (9.59) 1,034 (1.76) 1,367 (2.32) 1,564 (2.66) 1,795 (3.05) 0.32 13–16 2,281 (9.23) 1,137 (1.77) 1,466 (2.29) 1,664 (2.60) 1,862 (2.91) 0.32 17–20 2,162 (8.06) 1,097 (1.54) 1,428 (2.01) 1,621 (2.28) 1,876 (2.64) 0.33 Level of Urbanization Seoul 2,377 (9.08) 1,131 (1.50) 1,507 (2.00) 1,714 (2.27) 1,995 (2.64) 0.29 Metropolitan 2,887 (8.96) 1,247 (1.65) 1,649 (2.18) 1,911 (2.53) 2,190 (2.90) 0.32 City 5,386 (9.32) 2,747 (1.81) 3,583 (2.36) 4,068 (2.68) 4,633 (3.05) 0.33 County 798 (10.27) 427 (1.89) 542 (2.40) 600 (2.66) 685 (3.04) 0.30 a: (The number of women diagnosed with carcinoma in situ (D06)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100 b: (The cumulative number of women diagnosed with carcinoma in situ (D06/The number of women who did not undergo confirmatory test within 6 months after abnormal Pap smear) * 100 c: Percent to ratio of patients diagnosed with carcinoma in situ after 6 months compared to those diagnosed within 6 months of an abnormal Pap smear. d: The age at which the Pap smear was conducted. Table 5 The relative risk of being diagnosed with carcinoma in situ (D06) Characteristics Within 6 months a Until 2nd year b Until 3rd year b Until 4th year b Until 5th year b Age c (years old) 30–39 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 40–49 0.87(0.83–0.91) 0.83(0.77–0.89) 0.84(0.79–0.89) 0.84(0.79–0.88) 0.82(0.78–0.87) 50–59 0.57(0.54–0.60) 0.52(0.48–0.57) 0.54(0.50–0.58) 0.54(0.51–0.58) 0.55(0.51–0.58) 60–69 0.76(0.71–0.80) 0.72(0.66–0.78) 0.74(0.69–0.80) 0.75(0.70–0.80) 0.76(0.72–0.82) Over 70 1.03(0.96–1.11) 0.63(0.57–0.71) 0.70(0.64–0.77) 0.68(0.62–0.74) 0.70(0.64–0.76) Economic level 0–4 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 5–8 0.97(0.92–1.03) 0.97(0.89–1.06) 1.03(0.96–1.11) 1.01(0.95–1.08) 1.02(0.96–1.09) 9–12 0.97(0.92–1.03) 1.00(0.92–1.09) 1.03(0.96–1.11) 1.02(0.95–1.09) 1.02(0.96–1.09) 13–16 0.94(0.89–0.99) 1.01(0.93–1.10) 1.01(0.94–1.09) 1.00(0.93–1.06) 0.97(0.91–1.03) 17–20 0.85(0.81–0.90) 0.88(0.81–0.95) 0.89(0.83–0.95) 0.87(0.82–0.93) 0.88(0.83–0.94) Level of Urbanization Seoul 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) Metropolitan 0.99(0.94–1.04) 1.10(1.02–1.19) 1.09(1.02–1.17) 1.11(1.04–1.19) 1.10(1.03–1.16) City 1.03(0.98–1.08) 1.21(1.13–1.29) 1.18(1.11–1.25) 1.18(1.11–1.25) 1.15(1.09–1.21) County 1.13(1.05–1.22) 1.26(1.13–1.41) 1.20(1.09–1.33) 1.17(1.07–1.28) 1.15(1.05–1.25) a: (The number of women diagnosed with uterine cervical cancer (C56)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100 b: (The cumulative number of women diagnosed with uterine cervical cancer (C56)/The number of women who did not undergo a confirmatory test within 6 months after abnormal Pap smear) * 100 c: The age at which the Pap smear was conducted. Table 6 provides a breakdown of the percent ratio of cervical cancer diagnoses compared to carcinoma in situ. Among women who participated in a detailed examination within 6 months of being notified of the Abnormal Pap results, the percent ratio of being diagnosed with cervical cancer compared to carcinoma in situ of cervix was 39.5%. Analyzing age-related patterns, the highest ratio was observed in those aged 70 and above at 81.1%, while women in their 30s had the lowest ratio at 21.1%. Examining economic disparities, the highest ratio was found in the low-income group (economic level 0–4) at 44.1%. Although no clear trend was observed based on the degree of urbanization, the county with the least urbanization exhibited the highest percent ratio of transitioning to cervical cancer diagnoses at 48.1%. Table 6. The percent ratio of number of women diagnosed with uterine cervical cancer (C56) compared to carcinoma in situ (D06). Characteristics Within 6 months a Until 2nd year b Until 3rd year b Until 4th year b Until 5th year b Age c (years old) 30–39 21.1% 25.3% 27.6% 28.5% 28.5% 40–49 32.9% 43.3% 44.9% 45.2% 46.4% 50–59 52.5% 72.1% 70.4% 72.3% 70.3% 60–69 58.8% 74.2% 71.6% 71.8% 69.7% Over 70 81.1% 118.0% 106.5% 111.8% 109.3% Economic level 0–4 44.1% 61.5% 63.7% 63.8% 63.8% 5–8 39.1% 55.5% 53.6% 56.3% 54.7% 9–12 40.3% 54.4% 53.0% 53.6% 53.8% 13–16 34.6% 48.1% 49.6% 50.1% 52.4% 17–20 39.1% 49.8% 49.0% 50.0% 48.2% Level of Urbanization Seoul 35.3% 47.5% 47.2% 48.0% 46.4% Metropolitan 41.8% 66.7% 66.3% 66.6% 66.0% City 38.8% 48.9% 49.2% 50.0% 50.7% County 48.1% 61.8% 63.8% 69.0% 68.8% a: (The number of women diagnosed with carcinoma in situ (D06)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100 b: (The cumulative number of women diagnosed with carcinoma in situ (D06/The number of women who did not undergo confirmatory test within 6 months after abnormal Pap smear) * 100 c: The age at which the Pap smear was conducted. Tracking women who did not participate in confirmatory test within 6 months for up to 5 years, percent ratio of those diagnosed with cervical cancer compared to carcinoma in situ was 54.4%. The likelihood of being diagnosed with cervical cancer relative to carcinoma in situ increased with age and lower economic status. In women in their 30s, the percent ratio was 28.5%, with the probability of cervical cancer diagnosis increasing with age. In women in their 70s, the percent ratio was 109.3%, indicating a higher likelihood of being diagnosed with cervical cancer than carcinoma in situ. The lowest economic group (level 0–4) had a percent ratio of 63.8%, decreasing with improved economic status, with the highest economic group (level 17–20) at 48.2%. The least urbanized county had the highest percentage rate of cervical cancer diagnosed at 68.8%. Discussion In this study, we compared the diagnosis rates of cervical cancer and carcinoma in situ between women who received Abnormal Pap notifications and participated in confirmatory tests within 6 months and those who did not. We also analyzed the differences based on age, economic status, and the level of urbanization. While there are big data studies on Pap smear participation rates, research specifically focusing on women with abnormal Pap results and their participation in confirmatory tests is scarce. Our study revealed that women in their 30s had the highest participation rates in confirmatory tests, and as age increased, the participation rates declined. This trend aligns consistently with Pap smear screening participation rates. ( 8 ) The rate of diagnosis for cervical cancer increased with age among women who participated in confirmatory tests within 6 months. Even when tracking women who did not participate in confirmatory tests within 6 months for 5 years, the diagnosis rate for cervical cancer remained higher in older age groups. At the 5-year mark, the highest diagnosis rate was observed in the 70s. At the 5-year mark, the diagnosis rate was highest in the 70s; however, when compared to other age groups, the relative diagnosis rate was notably lower for those who underwent confirmatory tests within the 6 months prior to diagnosis. In South Korea, unlike Western countries where the frequency of cervical cancer decreases after the age of 50 to 60 ( 9 , 10 ), there is an increase in frequency with age.( 11 ) On the other hand, for carcinoma in situ, unlike cervical cancer, the diagnosis rate decreases with age until the age of 60 and increases again at the age of 70. Both cervical cancer and carcinoma in situ had higher diagnosis rates in populations with lower economic levels and lower levels of urbanization. Compared to women who underwent confirmatory tests within 6 months, the rate of diagnosis for Cervical cancer and carcinoma in situ was lower between 6 months and 5 years. However, when diagnosed, the relative proportion of diagnoses as cervical cancer, as opposed to carcinoma in situ, was higher. The diagnosis rate of cervical cancer was higher in the elderly, low-income groups, and areas with lower urbanization levels. Particularly in those aged 70 and above, there were more diagnoses of cervical cancer than cervical dysplasia. In cases diagnosed after 6 months, visits to the hospital were likely prompted by symptoms such as bleeding, rather than being asymptomatic, leading to a relatively higher proportion of diagnoses as cervical cancer. When cervical cancer was diagnosed, if it was discovered 6 months after the initial diagnosis, there might be a higher likelihood of the disease being at a more advanced stage than when discovered within the first 6 months, suggesting the need for further research in the future. In this study, only 27.6% underwent confirmatory tests, a lower percentage compared to existing studies ranging from 43–64%. ( 12 ) These prior studies ( 2 , 13 , 14 ) often targeted a small number of patients from a single hospital, differing from the nationwide screenings in this research. When education pamphlets, phone calls, and letters were used to explain the necessity, the rate of receiving confirmatory tests increased to 64–72%, compared to a simple notification. Repetitive reminders increased the rate of confirmatory tests, emphasizing the effectiveness of phone-based reminders. However, implementing nationwide calls for all women with abnormal pap smears is not practically feasible. Selective approaches should be considered. Given the higher diagnosis rates of cervical cancer in low-income and elderly women in this study, encouraging repeated testing through mobile interventions when HSIL or higher abnormal pap smears are detected in these groups could be explored. Low-income individuals, smokers, and people with disabilities exhibit lower cervical cancer screening rates, and their confirmatory test rates drop when abnormal findings are present. Factors such as age and economic considerations ( 15 – 17 ), which contribute to not participating in cervical cancer screening, can overlap with reasons for avoiding confirmatory tests after receiving abnormal pap smear notifications, possibly driven by the fear associated with cancer ( 18 ). The participation rate in confirmatory tests among the elderly was low. For many elderly women with physical disability, getting confirmatory test can be quite challenging. These women have reported difficulty in securing reliable transportation, locating clinics with specialized equipment, and entering a doctor’s office. While other studies ( 19 , 20 ) conducted in Western countries have suggested a relationship between younger age and a decreased likelihood of follow-up after an abnormal Pap smear. There was no difference in confirmatory test participation rates based on economic factors, as South Korea's healthcare insurance system is well-established, indicating that cost is not a barrier. Factors such as occupation, hormone therapy, and smoking could influence participation of confirmatory test. Female employees in South Korea are more likely to undergo cervical cancer screening as employers may hold responsibility for regular check-ups.( 17 ) However, it is not an obligation for employers to ensure additional tests for employees with abnormal findings during screenings. Women receiving hormone replacement therapy during menopause regularly visit gynecologists, increasing their likelihood of undergoing confirmatory tests. Smokers ( 17 ), known to disregard cervical cancer screening ( 21 ), might also exhibit a tendency to avoid confirmatory tests. These factors were not analyzed in this study. This study has several limitations. Firstly, it relied on surgical codes and disease codes rather than actual pathological results, which may introduce some degree of discrepancy with real outcomes. Since cervical cancer was confirmed using disease codes, it is likely that adenocarcinoma and adenosquamous carcinoma were included in addition to squamous cell carcinoma from a histopathological perspective. Considering that squamous cell carcinoma constitutes the majority of cervical cancer cases, the impact of this omission is deemed minimal.( 22 ) Following the ASCCP guidelines( 23 ), colposcopy and uterine cervical punch biopsy can be performed even when Pap smear results are normal but HPV is positive( 24 ) or when there are symptoms such as uterine bleeding. However, since this study is based on abnormal Pap results, such cases were excluded. Moreover, cases where hysterectomy was performed for conditions like uterine fibroids without undergoing cervical punch biopsy and were diagnosed with cervical dysplasia were also considered. Cases diagnosed with cervical cancer or cervical dysplasia based on disease codes, followed by confirmatory tests, were excluded from the study. Secondly, the study did not evaluate certain variables that could influence the implementation of confirmatory tests, such as the size of the hospital and the annual number of screenings. Thirdly, as the study is based on traditional Pap smear results, there might be differences in sensitivity and specificity compared to liquid-based cytology. Fourth, This study focused exclusively on squamous cell abnormalities, indicating a need for future research on glandular cell abnormalities. As far as we know, this is the first study utilizing big data to analyze the factors influencing participation in confirmatory tests following abnormal Pap smear results and the diagnostic rates of cervical cancer and carcinoma in situ. Despite being a large-scale study, liquid-based cytology was excluded, and the analysis was uniformly based on traditional Pap smear. Abnormal Pap smear results should be subsequently linked to confirmatory tests. The elderly and those with lower socioeconomic status exhibit a higher diagnosis rate of cervical cancer in South Korea, emphasizing the importance of increasing their participation rates. Particularly in the elderly, the participation rate in confirmatory tests is low, necessitating focused management for this group. Cervical cancer is prevalent in the South-East Asian region. The incidence rates and participation in confirmatory tests for cervical cancer and carcinoma in situ in this region differ from those in Western countries. The cervical cancer elimination program advocated by the World Health Organization (WHO) should be tailored to the specific characteristics of each country. Declarations Author Contribution Study concepts: K.H Study design: K.H Data acquisition: K.H Data analysis and interpretation: K.H., H.J.H. Statistical analysis: K.H., H.J.H Manuscript preparation: H.J.H. Manuscript editing: K.H, H.J.H. Manuscript review: K.H., H.J.H. Funding This research was supported by a fund from the research program of the Korea Medical Institute in 2022. Data availability This study used data from the National Health Insurance Service (NIHS) in South Korea, which is not publicly available due to privacy concerns. Data described in the manuscript are available from the corresponding author upon reasonable request. Conflict of interest The authors declare no conflicts of interest relevant to this study. References WHO. Accelerating the elimination of cervical cancer as a public health problem: Towards achieveing 90-70-90 targets by 2030 [Internet]. 2022 [cited December 2, 2023] Marcus AC, Kaplan CP, Crane LA, Berek JS, Bernstein G, Gunning JE et al (1998) Reducing loss-to-follow-up among women with abnormal Pap smears. Results from a randomized trial testing an intensive follow-up protocol and economic incentives. Med Care 36(3):397–410 Eggleston KS, Coker AL, Das IP, Cordray ST, Luchok KJ (2007) Understanding barriers for adherence to follow-up care for abnormal pap tests. J Womens Health (Larchmt) 16(3):311–330 Coker AL, Eggleston KS, Meyer TE, Luchok K, Das IP (2007) What predicts adherence to follow-up recommendations for abnormal Pap tests among older women? Gynecol Oncol 105(1):74–80 Battaglia TA, Santana MC, Bak S, Gokhale M, Lash TL, Ash AS et al (2010) Predictors of timely follow-up after abnormal cancer screening among women seeking care at urban community health centers. Cancer 116(4):913–921 Kim Y, Jun JK, Choi KS, Lee HY, Park EC (2011) Overview of the National Cancer screening programme and the cancer screening status in Korea. Asian Pac J Cancer Prev 12(3):725–730 Apgar BS, Zoschnick L, Wright TC (2003) Jr. The 2001 Bethesda System terminology. Am Fam Physician 68(10):1992–1998 Lee M, Chang HS, Park EC, Yu SH, Sohn M, Lee SG (2011) Factors associated with participation of Korean women in cervical cancer screening examination by age group. Asian Pac J Cancer Prev 12(6):1457–1462 Pedersen K, Fogelberg S, Thamsborg LH, Clements M, Nygard M, Kristiansen IS et al (2018) An overview of cervical cancer epidemiology and prevention in Scandinavia. Acta Obstet Gynecol Scand 97(7):795–807 Estenson L, Kim N, Jacobson M (2023) Do age-based discontinuation recommendations influence cervical cancer screening rates? Evidence from the United States' Behavioral Risk Factor Surveillance System, 2016 and 2018. Prev Med 172:107543 Park Y, Vongdala C, Kim J, Ki M (2015) Changing trends in the incidence (1999–2011) and mortality (1983–2013) of cervical cancer in the Republic of Korea. Epidemiol Health 37:e2015024 Khanna N, Phillips MD (2001) Adherence to care plan in women with abnormal Papanicolaou smears: a review of barriers and interventions. J Am Board Fam Pract 14(2):123–130 Lerman C, Hanjani P, Caputo C, Miller S, Delmoor E, Nolte S et al (1992) Telephone counseling improves adherence to colposcopy among lower-income minority women. J Clin Oncol 10(2):330–333 Stewart DE, Buchegger PM, Lickrish GM, Sierra S (1994) The effect of educational brochures on follow-up compliance in women with abnormal Papanicolaou smears. Obstet Gynecol 83(4):583–585 Lin SJ (2008) Factors influencing the uptake of screening services for breast and cervical cancer in Taiwan. J R Soc Promot Health 128(6):327–334 Ishii K, Tabuchi T, Iso H (2023) Trends in socioeconomic inequalities in cervical, breast, and colorectal cancer screening participation among women in Japan, 2010–2019. Cancer Epidemiol 84:102353 Park SJ, Park WS (2010) Identifying barriers to Papanicolaou smear screening in Korean women: Korean National Health and Nutrition Examination Survey 2005. J Gynecol Oncol 21(2):81–86 Uner FO, Korukcu O (2020) A prevalence and psychometric study on fear of cancer in women with abnormal cervical cytology undergoing colposcopy. Psychooncology 29(11):1850–1855 McKee MD, Lurio J, Marantz P, Burton W, Mulvihill M (1999) Barriers to follow-up of abnormal Papanicolaou smears in an urban community health center. Arch Fam Med 8(2):129–134 Peterson NB, Han J, Freund KM (2003) Inadequate follow-up for abnormal Pap smears in an urban population. J Natl Med Assoc 95(9):825–832 Fletcher FE, Vidrine DJ, Tami-Maury I, Danysh HE, King RM, Buchberg M et al (2014) Cervical cancer screening adherence among HIV-positive female smokers from a comprehensive HIV clinic. AIDS Behav 18(3):544–554 Suh DH, Ha HI, Lee YJ, Lim J, Won YJ, Lim MC (2023) Incidence and treatment outcomes of uterine cervical cancer in Korea 1999–2018 from the national cancer registry. J Gynecol Oncol 34(2):e39 2019 ASCCP (2020) Risk-Based Management Consensus Guidelines for Abnormal Cervical Cancer Screening Tests and Cancer Precursors: Erratum. J Low Genit Tract Dis 24(4):427 Kim M, Kim H, Suh DH, Kim YB (2021) Cervical Cancer in Women with Normal Papanicolaou Tests: A Korean Nationwide Cohort Study. Cancer Res Treat 53(3):813–818 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3856728","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267308468,"identity":"45e460c5-4554-45f8-825d-652f933f8cb0","order_by":0,"name":"Hyeongsu Kim","email":"","orcid":"","institution":"Konkuk University","correspondingAuthor":false,"prefix":"","firstName":"Hyeongsu","middleName":"","lastName":"Kim","suffix":""},{"id":267308469,"identity":"ce752810-da0a-45cf-b275-47380d444f40","order_by":1,"name":"Jong Ha Hwang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACxhnsBx984JGQMwBzDSyI0cKTbDhDxsYYqkUCSDAT0CPBYCbMYZOWuAHGJaiFeXZDGjNDzuH07exnj274USDBYM7efwC/w+YcPPa44Mzh3J09eWk3e4AOs+w5TMgvCenGM3sO5244kGN2gweoxeBGMkEtZtK8/w6nG5x/Y3bzD0jL/cdEaOHhSUswuJFjdhtiCwHvM87IAQYyj43hhhtvzG7LGEjwGJxJNsCrxXBGOjgq5Q3O55jdfPPHRs7g+MEH+LU0oAnw4HcVEMgTVDEKRsEoGAWjAADYOkmzOJNg+wAAAABJRU5ErkJggg==","orcid":"","institution":"International St. Mary’s Hospital, Catholic Kwandong University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jong","middleName":"Ha","lastName":"Hwang","suffix":""}],"badges":[],"createdAt":"2024-01-12 11:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3856728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3856728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49764083,"identity":"10b097f7-c052-459c-a0d5-07f866388392","added_by":"auto","created_at":"2024-01-17 16:24:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":565510,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart analyzing epithelial abnormalities of squamous cell between 2011 and 2017.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3856728/v1/3cde47eee5134740cb4e3c43.jpg"},{"id":49774798,"identity":"8200a6c5-85c9-4b93-b760-0df1d1c090f9","added_by":"auto","created_at":"2024-01-17 19:52:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":432679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3856728/v1/10c80337-ac40-4cc0-8fd8-a6533e7e913b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Diagnosis Rate and Factors Influencing the Implementation of Confirmatory Tests in Women with Epithelial Abnormalities of Squamous Cells in Pap Tests: 5 years follow-up using the National Health Insurance Service database. ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer, a significant global health concern, necessitates comprehensive screening programs for early detection and effective management. The World Health Organization (WHO) has adopted a cervical cancer elimination strategy as a public health initiative, aiming to achieve the 90-70-90 targets by 2030. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Confirmatory tests following abnormal Pap results are crucial in this context, providing a gateway to precise diagnosis and timely intervention.\u003c/p\u003e \u003cp\u003eWhen notified of an abnormal Pap smear result, approximately 53–75% of the general population undergoes appropriate follow-up for confirmatory tests. (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The decision for confirmatory testing is influenced by various factors, including the severity of the Pap test, age, smoking, race, type of insurance, distance to the hospital, and more. (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Understanding how these factors interact is crucial to enhancing the effectiveness of cervical cancer screening programs. Existing studies have predominantly been conducted in Western countries, often centered around single institutions or hospitals, making it challenging to find nationwide studies using big data from national screening programs.\u003c/p\u003e \u003cp\u003eThis study is based on South Korea's National Health Insurance Service database. The study analyzed variables influencing the performance of confirmatory tests for cervical cancer following the notification of an abnormal Pap smear result. The study also divides participants into groups based on whether they underwent confirmatory tests and tracks them for five years. By comparing the diagnosis rates of cervical cancer and carcinoma in situ between the two groups over time, the study aims to identify differences in diagnosis rates and contribute insights to improve existing cervical cancer screening strategies for better health outcomes.\u003c/p\u003e "},{"header":"Patients and Methods","content":"\u003ch2\u003e1. Study design and data source\u003c/h2\u003e\u003cp\u003eWe obtained data of Papanicolaou (Pap) smear, which is the screening test for uterine cervical cytology, from the National Health Insurance Service (NIHS) data. The health insurance data filed for medical claim was extracted from the NIHS database (Approval no. NHIS-2023-1-614). South Korea Government provide free Pap smear screenings every two years for all Korean women aged 20 and above since 2015. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) The conventional Pap smear was used for uterine cervical cancer screening. We excluded liquid based cervical cytology to screen uterine cervical cancer that is not covered by Korean medical insurance.\u003c/p\u003e\u003ch2\u003e2. Study population\u003c/h2\u003e\u003cp\u003eAll South Korean women, who were diagnosed with abnormal pap smear, were identified from the NHIS database between January 2011 and December 2017. Women over 30 who participated in population-based cervical cancer screening programs were included. The Korean government implements a cervical cancer screening program on a biennial basis. Eligibility for screening is determined by the birth year of women, with those born in odd-numbered years eligible for screening in odd-numbered years and those born in even-numbered years eligible for screening in even-numbered years. In cases where women do not undergo screening in the designated year, they have the option to apply for an extension and receive the screening the following year. However, for the purpose of this study, women who applied for an extension and underwent the screening in the subsequent year were excluded from the analysis.\u003c/p\u003e\u003cp\u003eDisease codes (C53: uterine cervical cancer, D06: carcinoma in situ, N87: cervical dysplasia, C54: endometrial cancer, C56: ovarian cancer) were used to exclude women who have received a diagnosis of gynecologic cancer and cervical dysplasia within 5 years from the date of Pap smear. Disease codes were standardized according to the Korea Standard Classification of Disease-6 (KCD-6), which is a modification of the International Classification of Disease and Related Health Problems, 10th edition (ICD-10), suited to Korean medical circumstances. The patients who underwent total hysterectomy were not included. Korea Health Insurance Review \u0026amp; Assessment Service (HIRA) released procedure and operation codes annually. The women without uterus were identified using operation codes (R0141, R0142, R4147, R4148, R4149, and R4140) including hysterectomy.\u003c/p\u003e\u003cp\u003ePap test outcomes underwent categorization according to the pathological findings delineated by the 2001 Bethesda system.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Normal Pap test outcomes were construed as those denoted as negative for intraepithelial lesions (NIL) or reactive cellular change (RCC). Abnormal Pap results pertaining to squamous cell manifestations were construed as those identified as atypical squamous cell of undetermined significance (ASCUS), atypical squamous cell with inconclusive exclusion of high-grade squamous intraepithelial lesion (ASC-H), Low-grade squamous intraepithelial lesion (LSIL), High-grade squamous cell intraepithelial lesion (HSIL), and Squamous cell carcinoma (SCC).\u003c/p\u003e\u003cp\u003ePatient attributes encompassed their age during the screening examination, financial standing ascertained through insurance categorization, the presence of enduring ailments, and their residency. Age was treated as a categorical parameter and compartmentalized into the subsequent brackets: 30–39, 40–49, 50–59, 60–69, and 70 years or more. In South Korea, health insurance premiums are computed by the government predicated on an individual's financial capacity. Economic status was stratified into four cohorts contingent on the spectrum of health insurance premium disbursements: 0–4 range, 5–8 range, 9–12 range, 13–16 range, and 17–20 range, with a higher numeral indicative of a superior economic standing. Residency was sorted into clusters comprising Seoul, metropolitan city, city, and county.\u003c/p\u003e\u003ch2\u003e3. Identification of confirmatory test in patients who have abnormal glandular Pap results and follow-up\u003c/h2\u003e\u003cp\u003eConfirmatory examinations comprised human papillomavirus (HPV) testing [PCR-based: D6586, C6033, DNA chip: C6031, D6592], cervical punch biopsy [C8570], and colposcopy [E7722: colposcopy with endocervical speculum, E7721: colposcopy without endocervical speculum]. In cases where Pap results indicated ASC-US, fulfillment of at least one of the ensuing assessments—cervical punch biopsy, colposcopy, or HPV test—was operationally construed as conducting a confirmatory test. For Pap results denoting ASC-H, LSIL, HSIL, and SCC, undertaking a cervical punch biopsy or colposcopy was operationally construed as conducting a confirmatory test. The computation sought to ascertain the count of women subjected to a confirmatory test within six months from the communication of abnormal Pap smear findings. Furthermore, scrutiny extended to discerning the presence of cervical pathology within 6 to 60 months among individuals with abnormal Pap smear results who refrained from undergoing a confirmatory test within the initial 6 months.\u003c/p\u003e\u003ch2\u003e4. Statistical analysis\u003c/h2\u003e\u003cp\u003eThe abnormal Pap rate, implementation rate of confirmatory test following abnormal Pap smears, and the percentage ratio of women diagnosed with uterine cervical cancer (C56) in comparison to carcinoma in situ (D06) were systematically documented and subjected to statistical scrutiny utilizing Microsoft Excel 365. The relative risk of uterine cervical cancer (C56) and carcinoma in situ (D06) diagnoses was computed for each age group (30–39 years), economic stratum (0–4), and urbanization level (Seoul) as the baseline, employing the analytical tools provided by MedCalc®.\u003c/p\u003e\u003ch2\u003e5. Ethics statement\u003c/h2\u003e\u003cp\u003e The Institutional Review Board of Konkuk University School of Medicine, Seoul, Republic of Korea (7001355-202210-E-179) reviewed and approved this study. Informed consent was not required from the subjects as the study utilized publicly available secondary data from NHIS.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure 1 presents the flowchart of the study. A total of 28,619,210 Pap smear tests were conducted between 2011 and 2017. Among the 21,452,058 eligible patients who met the predetermined criteria, 450,349 (1.57%) had squamous cell abnormalities in Pap smear results. Among women notified of squamous cell abnormalities, 124,135 (27.6%) underwent a confirmatory test within 6 months. Among these, there were 4,529 women (3.65%) diagnosed with cervical cancer (C53), and 11,465 women (9.24%) diagnosed with carcinoma in situ (D06). The number of women who did not undergo confirmatory tests within 6 months was 326,214 (72.4%). Among them, within 5 years, 5,198 women (1.59%) were diagnosed with cervical cancer (C53), and 9,517 women (9.24%) were diagnosed with carcinoma in situ (D06).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1 displays the rates of confirmatory tests based on age, economic status, and residential area for women with abnormal Pap results. The participation rate for confirmatory tests was highest in the 30s, reaching 32.7%, and decreased with increasing age, with the lowest rate observed in those aged 70 and above at 21.4%. Across all age groups, there was an increasing trend in the participation rate for confirmatory tests over the study period, but the increment was the smallest in the 70 and above age group, with only 11.9%. Examining the differences based on economic status, the participation rate was highest in the middle-income bracket (level 9\u0026ndash;12), although the variation compared to other income brackets was not substantial, ranging from 0.3\u0026ndash;1.3%. Regarding residential differences, women residing in Metropolitan areas had the highest participation rate at 29.9%, while those in county areas had the lowest at 25.6%. Throughout the study period, the increase in participation rate was most pronounced among women residing in county areas at 15.5%.\u003c/p\u003e\n\u003cp\u003eTables\u0026nbsp;2 and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the cervical cancer diagnosis rates and relative risk based on the participation in confirmatory test, age, economic status, and degree of urbanization. Among women who underwent confirmatory test within 6 months after notification of abnormal Pap results, the rate of cervical cancer diagnosis was lowest in women in their 30s at 2.37%, with 732 cases. As age increased, the diagnosis rate also increased, reaching the highest at 9.38% (710 cases) in women aged 70 and above (Relative risk 4.11, 95% CI: 3.72\u0026ndash;4.54). The diagnosis rate of cervical cancer was higher in groups with lower economic status and in areas with lower urbanization. When examining the rate of cervical cancer diagnosed within 5 years after 6 months of non-participation in confirmatory test, it was lowest in women in their 30s at 1.09%, and highest in women in their 70s at 2.91%. Comparing the rate of diagnosis after 6 months with participation in precision examinations to the rate after 6 months without participation, women in their 70s had the lowest at 0.31%. Examining the rate of cervical cancer diagnoses within 5 years among women who did not undergo a confirmatory test within 6 months, the rate was lowest in women in their 30s at 1.09% and highest in women in their 70s at 2.91%. The percentage ratio of patients diagnosed with cervical cancer after 6 months compared to those diagnosed within 6 months of an abnormal Pap smear was lowest in women in their 70s at 0.31%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eTable\u0026nbsp;1. The implementation rate of a confirmatory test in patients with abnormal Pap smear, N (%\u003csup\u003ea\u003c/sup\u003e).\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCharacteristics\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2011\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2012\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2013\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2014\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2015\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2016\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2017\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eTotal\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAge\u003csup\u003eb\u003c/sup\u003e (years old)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30\u0026ndash;39\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,640(25.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,160(27.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,068(25.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,923(30.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,716(33.12)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6,468(36.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7,898(38.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30891 (32.7)\u003c/div\u003e\n 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align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e38602 (27.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50\u0026ndash;59\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,385(20.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,391(19.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,120(19.6\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,881(23.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,644(27.19)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,695(30.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7,584(34.2)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30700 (26.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e60\u0026ndash;69\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,153(18.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,232(19.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,431(18.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,885(21.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,051(27.46)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,264(31.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,360(34.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e16376 (26.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eOver 70\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e578(16.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e607(16.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e729(16.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e949(18.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,286(22.09)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,468(24.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,949(28.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7566 (21.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eEconomic status\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0\u0026ndash;4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,842(20.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,849(20.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,703(20.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,077(23.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,863(28.39)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,769(31.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6,294(34.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e25397 (26.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5\u0026ndash;8\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,500(20.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,572(21.2)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,092(21.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,723(25.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,861(29.01)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,383(32.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,473(35.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e21604 (27.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e9\u0026ndash;12\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,552(21.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,723(22.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,224(21.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,906(26.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,214(28.78)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,551(32.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,870(35.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e23040 (28.2)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e13\u0026ndash;16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,728(21.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,891(21.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,505(21.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,189(25.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,523(28.73)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,825(32.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6,054(35.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e24715 (27.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e17\u0026ndash;20\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,055(21.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,235(21.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,734(21.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,524(24.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,838(28.34)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,151(31.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6,329(35.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e26866 (27.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eResidence\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSeoul\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,897(20.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,061(19.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,850(20.2)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,523(23.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,760(25.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,834(29.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6,265(34.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e26190 (25.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMetropolitan\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,287(21.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2,584(24.3)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3,284(22.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,121(27.48)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,973(31.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6,325(36.1)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7,639(36.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e32213 (29.9)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCity\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,089(21.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4,267(21.4)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e5,648(21.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7,076(24.49)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e10,520(28.53)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e11,444(32.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14,753(34.8)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e57797 (27.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCounty\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e565(17.5)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e617(19.7)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e793(20.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e975(23.52)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,399(27.31)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,530(30.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1,889(33.0)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7768 (25.6)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ea: (The number of women who underwent a confirmatory test within 6 months after Pap smear/The number of women who underwent a Pap smear in the current year) * 100\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eb: The age at which the Pap smear was conducted.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable\u0026nbsp;2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe number of women diagnosed with uterine cervical cancer (C56), N (%).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithin 6 months\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 2nd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 3rd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 4th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 5th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge\u003csup\u003ed\u003c/sup\u003e (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e732 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e513 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e605 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e694 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,237 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e824 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,111 (1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,273 (1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,469 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,032 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e751 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e968 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,135 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,278 (1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e818 (5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e728 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e837 (1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e950 (2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e710 (9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e473 (1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e602 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e702 (2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,103 (4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e746 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e996 (1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,151 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,320 (1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e812 (3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e529 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e699 (1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e831 (1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e935 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891 (3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e563 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e725 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e839 (1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e966 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e790 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e547 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e727 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e834 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e975 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845 (3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e810 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of Urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeoul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e839 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e537 (0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e822 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e925 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,208 (3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e832 (1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,093 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,273 (1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,445 (1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,092 (3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,344 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,764 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,035 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,349 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264 (1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346 (1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e414 (1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e471 (2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003ea: (The number of women diagnosed with uterine cervical cancer (C56)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eb: (The cumulative number of women diagnosed with uterine cervical cancer (C56)/The number of women who did not undergo a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003ec: Percent to ratio of patients diagnosed with cervical cancer after 6 months compared to those diagnosed within 6 months of an abnormal Pap smear.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ed: The age at which the Pap smear was conducted.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003cstrong\u003eTable 3.\u003c/strong\u003e\u0026nbsp; The relative risk of being diagnosed with uterine cervical cancer (C56)\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWithin 6 months\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 2nd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 3rd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 4th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 5th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge\u003csup\u003ec\u003c/sup\u003e (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35(1.24\u0026ndash;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42(1.25\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37(1.23\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33(1.21\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34(1.22\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42(1.29\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49(1.32\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37(1.24\u0026ndash;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37(1.24\u0026ndash;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35(1.23\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11(1.91\u0026ndash;2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10(1.84\u0026ndash;2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94(1.73\u0026ndash;2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89(1.70\u0026ndash;2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87(1.69\u0026ndash;2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11(3.72\u0026ndash;4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95(2.58\u0026ndash;3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70(2.40\u0026ndash;3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67(2.39\u0026ndash;2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67(2.42\u0026ndash;2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87(0.79\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.77\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.80\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.82\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.81\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.82\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.80\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87(0.79\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86(0.79\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86(0.79\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74(0.67\u0026ndash;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79(0.71\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80(0.73\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78(0.72\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80(0.73\u0026ndash;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73(0.66\u0026ndash;0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71(0.64\u0026ndash;0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69(0.63\u0026ndash;0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68(0.63\u0026ndash;0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67(0.61\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of Urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeoul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17(1.07\u0026ndash;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55(1.39\u0026ndash;1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53(1.40\u0026ndash;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55(1.42\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56(1.44\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13(1.04\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24(1.12\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23(1.13\u0026ndash;1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23(1.13\u0026ndash;1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26(1.17\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54(1.37\u0026ndash;1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64(1.42\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63(1.43\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68(1.50\u0026ndash;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70(1.53\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003ea: (The number of women diagnosed with uterine cervical cancer (C56)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eb: (The cumulative number of women diagnosed with uterine cervical cancer (C56)/The number of women who did not undergo a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003ec: The age at which the Pap smear was conducted.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTables\u0026nbsp;4 and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e depict the rates of carcinoma in situ diagnoses and the relative risk associated with confirmatory test participation, age, economic status, and degree of urbanization. Among women who participated in confirmatory test within 6 months after receiving abnormal Pap results, the incidence of carcinoma in situ was highest in women in their 70s at 11.58% (876 cases), followed by women in their 30s at 11.23% (3,469 cases). There was no statistically significant difference between women in their 30s and 70s (RR 1.03, 95% CI: 0.96\u0026ndash;1.11). Notably, the group with the highest economic status (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e) exhibited a significantly lower diagnostic rate at 8.06% (2,162 cases) (RR 0.85, 95% CI: 0.81\u0026ndash;0.90). In areas with lower urbanization, specifically in counties, the diagnosis rate of carcinoma in situ was significantly higher at 10.27% (798 cases) (RR 1.13, 95% CI: 1.05\u0026ndash;1.22).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable\u0026nbsp;4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe number of women diagnosed with carcinoma in situ (D06), N (%).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithin 6 months\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 2nd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 3rd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 4th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUntil 5th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge\u003csup\u003ed\u003c/sup\u003e (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,469 (11.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,453 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,862 (2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,126 (3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,433 (3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,764 (9.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,902 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,474 (2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,816 (2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,166 (3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,966 (6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,042 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,375 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,570 (1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,817 (2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,390 (8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e764 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,017 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,166 (2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,363 (2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e876 (11.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e565 (2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e628 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e738 (2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,503 (9.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,213 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,563 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,803 (2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,069 (2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,075 (9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e953 (1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,305 (2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,475 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,710 (3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,209 (9.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,034 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,367 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,564 (2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,795 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,281 (9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,137 (1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,466 (2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,664 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,862 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,162 (8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,097 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,428 (2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,621 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,876 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of Urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeoul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,377 (9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,131 (1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,507 (2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,714 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,995 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,887 (8.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,247 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,649 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,911 (2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,190 (2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,386 (9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,747 (1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,583 (2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,068 (2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,633 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e798 (10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542 (2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600 (2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e685 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003ea: (The number of women diagnosed with carcinoma in situ (D06)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eb: (The cumulative number of women diagnosed with carcinoma in situ (D06/The number of women who did not undergo confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003ec: Percent to ratio of patients diagnosed with carcinoma in situ after 6 months compared to those diagnosed within 6 months of an abnormal Pap smear.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ed: The age at which the Pap smear was conducted.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable\u0026nbsp;5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relative risk of being diagnosed with carcinoma in situ (D06)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWithin 6 months\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 2nd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 3rd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 4th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 5th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge\u003csup\u003ec\u003c/sup\u003e (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87(0.83\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83(0.77\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84(0.79\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84(0.79\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82(0.78\u0026ndash;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57(0.54\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52(0.48\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54(0.50\u0026ndash;0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54(0.51\u0026ndash;0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55(0.51\u0026ndash;0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76(0.71\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72(0.66\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74(0.69\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75(0.70\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76(0.72\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03(0.96\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63(0.57\u0026ndash;0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70(0.64\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68(0.62\u0026ndash;0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70(0.64\u0026ndash;0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97(0.92\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97(0.89\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03(0.96\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01(0.95\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02(0.96\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97(0.92\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.92\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03(0.96\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02(0.95\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02(0.96\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94(0.89\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01(0.93\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01(0.94\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.93\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97(0.91\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85(0.81\u0026ndash;0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.81\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.83\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87(0.82\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.83\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of Urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeoul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99(0.94\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10(1.02\u0026ndash;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09(1.02\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11(1.04\u0026ndash;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10(1.03\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03(0.98\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21(1.13\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18(1.11\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18(1.11\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15(1.09\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13(1.05\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26(1.13\u0026ndash;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20(1.09\u0026ndash;1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17(1.07\u0026ndash;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15(1.05\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003ea: (The number of women diagnosed with uterine cervical cancer (C56)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eb: (The cumulative number of women diagnosed with uterine cervical cancer (C56)/The number of women who did not undergo a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003ec: The age at which the Pap smear was conducted.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e provides a breakdown of the percent ratio of cervical cancer diagnoses compared to carcinoma in situ. Among women who participated in a detailed examination within 6 months of being notified of the Abnormal Pap results, the percent ratio of being diagnosed with cervical cancer compared to carcinoma in situ of cervix was 39.5%. Analyzing age-related patterns, the highest ratio was observed in those aged 70 and above at 81.1%, while women in their 30s had the lowest ratio at 21.1%. Examining economic disparities, the highest ratio was found in the low-income group (economic level 0\u0026ndash;4) at 44.1%. Although no clear trend was observed based on the degree of urbanization, the county with the least urbanization exhibited the highest percent ratio of transitioning to cervical cancer diagnoses at 48.1%.\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003cstrong\u003eTable 6.\u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe percent ratio of number of women diagnosed with uterine cervical cancer (C56) compared to carcinoma in situ (D06).\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWithin 6 months\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 2nd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 3rd year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 4th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUntil 5th year\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge\u003csup\u003ec\u003c/sup\u003e (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOver 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of Urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeoul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003ea: (The number of women diagnosed with carcinoma in situ (D06)/The number of women who underwent a confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eb: (The cumulative number of women diagnosed with carcinoma in situ (D06/The number of women who did not undergo confirmatory test within 6 months after abnormal Pap smear) * 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003ec: The age at which the Pap smear was conducted.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTracking women who did not participate in confirmatory test within 6 months for up to 5 years, percent ratio of those diagnosed with cervical cancer compared to carcinoma in situ was 54.4%. The likelihood of being diagnosed with cervical cancer relative to carcinoma in situ increased with age and lower economic status. In women in their 30s, the percent ratio was 28.5%, with the probability of cervical cancer diagnosis increasing with age. In women in their 70s, the percent ratio was 109.3%, indicating a higher likelihood of being diagnosed with cervical cancer than carcinoma in situ. The lowest economic group (level 0\u0026ndash;4) had a percent ratio of 63.8%, decreasing with improved economic status, with the highest economic group (level 17\u0026ndash;20) at 48.2%. The least urbanized county had the highest percentage rate of cervical cancer diagnosed at 68.8%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we compared the diagnosis rates of cervical cancer and carcinoma in situ between women who received Abnormal Pap notifications and participated in confirmatory tests within 6 months and those who did not. We also analyzed the differences based on age, economic status, and the level of urbanization. While there are big data studies on Pap smear participation rates, research specifically focusing on women with abnormal Pap results and their participation in confirmatory tests is scarce. Our study revealed that women in their 30s had the highest participation rates in confirmatory tests, and as age increased, the participation rates declined. This trend aligns consistently with Pap smear screening participation rates. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe rate of diagnosis for cervical cancer increased with age among women who participated in confirmatory tests within 6 months. Even when tracking women who did not participate in confirmatory tests within 6 months for 5 years, the diagnosis rate for cervical cancer remained higher in older age groups. At the 5-year mark, the highest diagnosis rate was observed in the 70s. At the 5-year mark, the diagnosis rate was highest in the 70s; however, when compared to other age groups, the relative diagnosis rate was notably lower for those who underwent confirmatory tests within the 6 months prior to diagnosis.\u003c/p\u003e \u003cp\u003eIn South Korea, unlike Western countries where the frequency of cervical cancer decreases after the age of 50 to 60 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), there is an increase in frequency with age.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) On the other hand, for carcinoma in situ, unlike cervical cancer, the diagnosis rate decreases with age until the age of 60 and increases again at the age of 70. Both cervical cancer and carcinoma in situ had higher diagnosis rates in populations with lower economic levels and lower levels of urbanization.\u003c/p\u003e \u003cp\u003eCompared to women who underwent confirmatory tests within 6 months, the rate of diagnosis for Cervical cancer and carcinoma in situ was lower between 6 months and 5 years. However, when diagnosed, the relative proportion of diagnoses as cervical cancer, as opposed to carcinoma in situ, was higher. The diagnosis rate of cervical cancer was higher in the elderly, low-income groups, and areas with lower urbanization levels. Particularly in those aged 70 and above, there were more diagnoses of cervical cancer than cervical dysplasia. In cases diagnosed after 6 months, visits to the hospital were likely prompted by symptoms such as bleeding, rather than being asymptomatic, leading to a relatively higher proportion of diagnoses as cervical cancer. When cervical cancer was diagnosed, if it was discovered 6 months after the initial diagnosis, there might be a higher likelihood of the disease being at a more advanced stage than when discovered within the first 6 months, suggesting the need for further research in the future.\u003c/p\u003e \u003cp\u003eIn this study, only 27.6% underwent confirmatory tests, a lower percentage compared to existing studies ranging from 43\u0026ndash;64%. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) These prior studies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) often targeted a small number of patients from a single hospital, differing from the nationwide screenings in this research. When education pamphlets, phone calls, and letters were used to explain the necessity, the rate of receiving confirmatory tests increased to 64\u0026ndash;72%, compared to a simple notification. Repetitive reminders increased the rate of confirmatory tests, emphasizing the effectiveness of phone-based reminders. However, implementing nationwide calls for all women with abnormal pap smears is not practically feasible. Selective approaches should be considered. Given the higher diagnosis rates of cervical cancer in low-income and elderly women in this study, encouraging repeated testing through mobile interventions when HSIL or higher abnormal pap smears are detected in these groups could be explored. Low-income individuals, smokers, and people with disabilities exhibit lower cervical cancer screening rates, and their confirmatory test rates drop when abnormal findings are present. Factors such as age and economic considerations (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which contribute to not participating in cervical cancer screening, can overlap with reasons for avoiding confirmatory tests after receiving abnormal pap smear notifications, possibly driven by the fear associated with cancer (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe participation rate in confirmatory tests among the elderly was low. For many elderly women with physical disability, getting confirmatory test can be quite challenging. These women have reported difficulty in securing reliable transportation, locating clinics with specialized equipment, and entering a doctor\u0026rsquo;s office. While other studies (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) conducted in Western countries have suggested a relationship between younger age and a decreased likelihood of follow-up after an abnormal Pap smear. There was no difference in confirmatory test participation rates based on economic factors, as South Korea's healthcare insurance system is well-established, indicating that cost is not a barrier. Factors such as occupation, hormone therapy, and smoking could influence participation of confirmatory test. Female employees in South Korea are more likely to undergo cervical cancer screening as employers may hold responsibility for regular check-ups.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) However, it is not an obligation for employers to ensure additional tests for employees with abnormal findings during screenings. Women receiving hormone replacement therapy during menopause regularly visit gynecologists, increasing their likelihood of undergoing confirmatory tests. Smokers (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), known to disregard cervical cancer screening (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), might also exhibit a tendency to avoid confirmatory tests. These factors were not analyzed in this study.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, it relied on surgical codes and disease codes rather than actual pathological results, which may introduce some degree of discrepancy with real outcomes. Since cervical cancer was confirmed using disease codes, it is likely that adenocarcinoma and adenosquamous carcinoma were included in addition to squamous cell carcinoma from a histopathological perspective. Considering that squamous cell carcinoma constitutes the majority of cervical cancer cases, the impact of this omission is deemed minimal.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Following the ASCCP guidelines(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), colposcopy and uterine cervical punch biopsy can be performed even when Pap smear results are normal but HPV is positive(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) or when there are symptoms such as uterine bleeding. However, since this study is based on abnormal Pap results, such cases were excluded. Moreover, cases where hysterectomy was performed for conditions like uterine fibroids without undergoing cervical punch biopsy and were diagnosed with cervical dysplasia were also considered. Cases diagnosed with cervical cancer or cervical dysplasia based on disease codes, followed by confirmatory tests, were excluded from the study. Secondly, the study did not evaluate certain variables that could influence the implementation of confirmatory tests, such as the size of the hospital and the annual number of screenings. Thirdly, as the study is based on traditional Pap smear results, there might be differences in sensitivity and specificity compared to liquid-based cytology. Fourth, This study focused exclusively on squamous cell abnormalities, indicating a need for future research on glandular cell abnormalities.\u003c/p\u003e \u003cp\u003eAs far as we know, this is the first study utilizing big data to analyze the factors influencing participation in confirmatory tests following abnormal Pap smear results and the diagnostic rates of cervical cancer and carcinoma in situ. Despite being a large-scale study, liquid-based cytology was excluded, and the analysis was uniformly based on traditional Pap smear. Abnormal Pap smear results should be subsequently linked to confirmatory tests. The elderly and those with lower socioeconomic status exhibit a higher diagnosis rate of cervical cancer in South Korea, emphasizing the importance of increasing their participation rates. Particularly in the elderly, the participation rate in confirmatory tests is low, necessitating focused management for this group. Cervical cancer is prevalent in the South-East Asian region. The incidence rates and participation in confirmatory tests for cervical cancer and carcinoma in situ in this region differ from those in Western countries. The cervical cancer elimination program advocated by the World Health Organization (WHO) should be tailored to the specific characteristics of each country.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concepts: K.H\u003c/p\u003e\n\u003cp\u003eStudy design: K.H\u003c/p\u003e\n\u003cp\u003eData acquisition: K.H\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: K.H., H.J.H.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: K.H., H.J.H\u003c/p\u003e\n\u003cp\u003eManuscript preparation: H.J.H.\u003c/p\u003e\n\u003cp\u003eManuscript editing: K.H, H.J.H.\u003c/p\u003e\n\u003cp\u003eManuscript review: K.H., H.J.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;research\u0026nbsp;was\u0026nbsp;supported\u0026nbsp;by\u0026nbsp;a\u0026nbsp;fund\u0026nbsp;from\u0026nbsp;the\u0026nbsp;research\u0026nbsp;program\u0026nbsp;of\u0026nbsp;the\u0026nbsp;Korea\u0026nbsp;Medical\u0026nbsp;Institute\u0026nbsp;in\u0026nbsp;2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from\u0026nbsp;the National Health Insurance Service (NIHS) in South Korea,\u0026nbsp;which is not publicly available due to privacy concerns. Data described in the manuscript are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest relevant to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Accelerating the elimination of cervical cancer as a public health problem: Towards achieveing 90-70-90 targets by 2030 [Internet]. 2022 [cited December 2, 2023]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcus AC, Kaplan CP, Crane LA, Berek JS, Bernstein G, Gunning JE et al (1998) Reducing loss-to-follow-up among women with abnormal Pap smears. Results from a randomized trial testing an intensive follow-up protocol and economic incentives. Med Care 36(3):397\u0026ndash;410\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEggleston KS, Coker AL, Das IP, Cordray ST, Luchok KJ (2007) Understanding barriers for adherence to follow-up care for abnormal pap tests. J Womens Health (Larchmt) 16(3):311\u0026ndash;330\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoker AL, Eggleston KS, Meyer TE, Luchok K, Das IP (2007) What predicts adherence to follow-up recommendations for abnormal Pap tests among older women? Gynecol Oncol 105(1):74\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBattaglia TA, Santana MC, Bak S, Gokhale M, Lash TL, Ash AS et al (2010) Predictors of timely follow-up after abnormal cancer screening among women seeking care at urban community health centers. Cancer 116(4):913\u0026ndash;921\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim Y, Jun JK, Choi KS, Lee HY, Park EC (2011) Overview of the National Cancer screening programme and the cancer screening status in Korea. Asian Pac J Cancer Prev 12(3):725\u0026ndash;730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApgar BS, Zoschnick L, Wright TC (2003) Jr. The 2001 Bethesda System terminology. Am Fam Physician 68(10):1992\u0026ndash;1998\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee M, Chang HS, Park EC, Yu SH, Sohn M, Lee SG (2011) Factors associated with participation of Korean women in cervical cancer screening examination by age group. Asian Pac J Cancer Prev 12(6):1457\u0026ndash;1462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen K, Fogelberg S, Thamsborg LH, Clements M, Nygard M, Kristiansen IS et al (2018) An overview of cervical cancer epidemiology and prevention in Scandinavia. Acta Obstet Gynecol Scand 97(7):795\u0026ndash;807\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstenson L, Kim N, Jacobson M (2023) Do age-based discontinuation recommendations influence cervical cancer screening rates? Evidence from the United States' Behavioral Risk Factor Surveillance System, 2016 and 2018. Prev Med 172:107543\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark Y, Vongdala C, Kim J, Ki M (2015) Changing trends in the incidence (1999\u0026ndash;2011) and mortality (1983\u0026ndash;2013) of cervical cancer in the Republic of Korea. Epidemiol Health 37:e2015024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanna N, Phillips MD (2001) Adherence to care plan in women with abnormal Papanicolaou smears: a review of barriers and interventions. J Am Board Fam Pract 14(2):123\u0026ndash;130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLerman C, Hanjani P, Caputo C, Miller S, Delmoor E, Nolte S et al (1992) Telephone counseling improves adherence to colposcopy among lower-income minority women. J Clin Oncol 10(2):330\u0026ndash;333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStewart DE, Buchegger PM, Lickrish GM, Sierra S (1994) The effect of educational brochures on follow-up compliance in women with abnormal Papanicolaou smears. Obstet Gynecol 83(4):583\u0026ndash;585\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin SJ (2008) Factors influencing the uptake of screening services for breast and cervical cancer in Taiwan. J R Soc Promot Health 128(6):327\u0026ndash;334\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshii K, Tabuchi T, Iso H (2023) Trends in socioeconomic inequalities in cervical, breast, and colorectal cancer screening participation among women in Japan, 2010\u0026ndash;2019. Cancer Epidemiol 84:102353\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark SJ, Park WS (2010) Identifying barriers to Papanicolaou smear screening in Korean women: Korean National Health and Nutrition Examination Survey 2005. J Gynecol Oncol 21(2):81\u0026ndash;86\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUner FO, Korukcu O (2020) A prevalence and psychometric study on fear of cancer in women with abnormal cervical cytology undergoing colposcopy. Psychooncology 29(11):1850\u0026ndash;1855\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKee MD, Lurio J, Marantz P, Burton W, Mulvihill M (1999) Barriers to follow-up of abnormal Papanicolaou smears in an urban community health center. Arch Fam Med 8(2):129\u0026ndash;134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson NB, Han J, Freund KM (2003) Inadequate follow-up for abnormal Pap smears in an urban population. J Natl Med Assoc 95(9):825\u0026ndash;832\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFletcher FE, Vidrine DJ, Tami-Maury I, Danysh HE, King RM, Buchberg M et al (2014) Cervical cancer screening adherence among HIV-positive female smokers from a comprehensive HIV clinic. AIDS Behav 18(3):544\u0026ndash;554\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuh DH, Ha HI, Lee YJ, Lim J, Won YJ, Lim MC (2023) Incidence and treatment outcomes of uterine cervical cancer in Korea 1999\u0026ndash;2018 from the national cancer registry. J Gynecol Oncol 34(2):e39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2019 ASCCP (2020) Risk-Based Management Consensus Guidelines for Abnormal Cervical Cancer Screening Tests and Cancer Precursors: Erratum. J Low Genit Tract Dis 24(4):427\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim M, Kim H, Suh DH, Kim YB (2021) Cervical Cancer in Women with Normal Papanicolaou Tests: A Korean Nationwide Cohort Study. Cancer Res Treat 53(3):813\u0026ndash;818\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer, Carcinoma in situ, squamous cell abnormalities, Korean nationwide cervical cancer screening program","lastPublishedDoi":"10.21203/rs.3.rs-3856728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3856728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e this study aims to investigate the diagnosis rates of cervical cancer and carcinoma in situ between women who did not undergo confirmatory tests within 6 months and those who did, considering influencing factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We identified all Korean women who participate in nationwide cervical cancer screening program from January 2011 and December 2017 using the National Health Insurance Service (NHIS) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong the 21,452,058 eligible patients from 28,619,210 Pap smear tests (2011-2017), 450,349 (1.57%) had squamous cell abnormalities in Pap smear results. Among women notified of squamous cell abnormalities, 124,135 (27.6%) underwent a confirmatory test within 6 months. Among these, there were 4,529 women (3.65%) diagnosed with cervical cancer (C53) and 11,465 women (9.24%) diagnosed with carcinoma in situ (D06). Women who did not undergo confirmatory tests within 6 months were 326,214 (72.4%). Among them, within 5 years, 5,198 women (1.59%) were diagnosed with cervical cancer (C53), and 9,517 women (9.24%) were diagnosed with carcinoma in situ (D06). For cervical cancer (RR 4.11, 95% CI: 3.72-4.54), the diagnosis rate was significantly higher in women over 70. Both cervical cancer (RR 0.73, 95% CI: 0.66-0.79) and carcinoma in situ (RR 0.85, 95% CI: 0.81-0.90) showed significantly lower diagnosis rates in highest economic status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eEncouraging confirmatory tests for abnormal Pap smears is crucial, particularly among the elderly and those with lower economic status.\u003c/p\u003e","manuscriptTitle":"Analysis of Diagnosis Rate and Factors Influencing the Implementation of Confirmatory Tests in Women with Epithelial Abnormalities of Squamous Cells in Pap Tests: 5 years follow-up using the National Health Insurance Service database. 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