The Role of Health Beliefs and HPV Self-Testing in Cervical Cancer Screening Participation in Czechia

The Role of Health Beliefs and HPV Self-Testing in Cervical Cancer Screening Participation in Czechia | 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 The Role of Health Beliefs and HPV Self-Testing in Cervical Cancer Screening Participation in Czechia Kristina Janousková, Jirí Frei, Denis Mainz, Juliana Melichova, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7719792/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2026 Read the published version in BMC Women's Health → Version 1 posted 16 You are reading this latest preprint version Abstract Objectives The aim of this study was to use the Champions Health Belief Model Scale (CHBM) to measure beliefs and attitudes influencing participation of eligible women in Czechia in the national cervical cancer screening program and to analyze the potential increase in motivation to participate in screening if a home-based HPV-self test would be available. Methods A cross-sectional population-based study was conducted on a representative sample of women eligible for cervical cancer screening in the Czech Republic in March-April 2025. Overall, 1500 women were recruited for the survey and interviewed using the CHBM. Results We found that 61% of eligible women reported prior participation in cervical cancer screening. Their participation was strongly predicted by perceived benefits (OR 1.6, 95%CI:1.3-2), self-efficacy (OR 1.6, 95%CI: 1.2-2) and perceived barriers (OR 0.4, 95%CI:0.32–0.51) even after adjusting for age, education and income. The availability of a home-based self-administered HPV test instead of a cytological exam would increase the motivation to participate in the screening program in 29% of the eligible women. This motivation is strongly predicted by the willingness of women to the self-test (OR 5.8, 95%CI:4.3–8.1) and covering the cost by health insurance (OR 2. 95%CI: 1.6–2.5), while fear of test misuse and the willingness to follow-up with the physician after the self-test reduced significantly this motivation – OR 0.65 (95%CI: 0.55–0.77) and 0.46 (95%CI: 0.36–0.59), respectively. Conclusions This study highlights the critical role of perceived barriers, benefits, and self-efficacy in driving cervical cancer screening participation in Czechia. The potential of self-administered HPV testing to increase motivation among underserved groups offers a promising strategy to enhance screening coverage. By addressing barriers, promoting benefits, and ensuring robust follow-up systems, Czechia can make significant progress toward meeting the WHO’s 70% coverage target and reducing its cervical cancer burden. These findings provide a foundation for targeted public health interventions and future research to advance cervical cancer prevention in the region. Cervical cancer Screening Participation Human Papilloma Virus Self-test Champions Health Belief Model Scale Introduction Cervical cancer poses a serious public health and societal problem globally. According to the Global Cancer Observatory (GLOBOCAN), as of 2022, cervical cancer ranks as 8 th in terms of incidence (with a global age standardized incidence rate of 14.1 per 100,000 and over 660,000 new cases annually) and 9 th in terms of mortality (with an annual mortality of 7.1 which translates into more than 348,000 annual deaths) [1]. While in Europe, the annual number of new cases is projected to decrease by 4.4% from 2022 to 2045 (attributable to decrease in population size), in other regions substantial increases are projected that are most apparent in low-income regions [2], confirming persisting global inequalities in incidence and mortality [3]. Thanks to the well-established causal link between human papilloma virus infection and cervical cancer [4, 5], there are effective strategies that could help to overturn these unfavorable projections [2]. Key stakeholders acted in this regard. In 2020, the World Health Organization (WHO) adopted the Global strategy to accelerate the elimination of cervical cancer as a public health problem [3] – an ambitious plan aiming to have 90% of girls vaccinated with HPV vaccine by age 15; 70% of women are screened with a high-performance test by 35 years and again by 45 years; and 90% of women identified with cervical disease receive treatment. Aligned with this strategy, Europe’s Beating Cancer Plan is a political commitment of the European Union (EU) to take action against cancer through ten flagship initiatives that include HPV vaccination and cervical cancer screening as key strategies to tackle cervical cancer in Europe [6]. Thus, besides vaccination, screening has been established as a key strategy in fighting cervical cancer. A recent systematic review summarizing studies from European countries showed a reduction of mortality due to cervical cancer of 41% to 92% in women attending population-based screening, compared to those not attending [7]. A modelling study in six European countries showed a projected 50-60% drop in incidence rates for a period between 2017-2040 if a screening program is introduced vs. no screening program [8]. The two major types of screening are cytology-based (Pap Smear) screening and HPV-based screening. While cytology-based screening has been shown to be very effective – a systematic review showed a decrease of incidence risk by over 60% [9], they are dependent on well-organized patient follow-up, additional diagnostics and the availability of patient management resources [3]. Their implementation is therefore problematic, especially in low- and middle-income countries. On the other hand, HPV-based screening provides clear added benefits over cytology: randomized trials showed that they provide 60–70% greater protection against invasive cervical carcinomas compared with cytology [10]; they have superior specificity, their strong negative predictive value allows for prolonged retest period [3]; and they are cost-effective under most scenarios in European settings [11] and countries outside Europe [12, 13]. In addition, HPV-based screening can be provided as a home-based self-test that can provide samples of similar quality as clinician samples and can increase participation of women in the screening, compared to invitations-based approaches [14]. It has been estimated that the implementation of HPV-based screening at age 35 and repeated at age 45 years with 70% coverage (as targeted by the WHO) globally, could avert between 12.5–13.4 millions of cervical cancer cases in the next 50 years [15]. Based on these facts, HPV-based screening is the method of choice that is advocated by WHO [3] and many countries of Europe are transitioning from cytology-based screening as the primary population-based screening method, with the Netherlands already fully transitioned [16]. Despite the evidence that shows their many benefits, countries are lagging in introducing population based cervical cancer screening programs, which is especially apparent in low- and middle-income countries (who)[3]. While by 2020 a population screening program for cervical cancer was introduced in 22 EU member states, in some countries they are still not fully implemented and large inequalities are observed in uptake ranging from 25% to 80% [6, 17]. In Czechia - while pap smears have been in use since the 1960’s [18] – a national based screening program using the method was only rolled out in 2008 [18-20]. In 2021 it was supplemented by an HPV test done at the time of the Pap smear at ages 35, 45 and since 2024 also at age of 55 years [21]. Thus, the country – in line with the evidence and recommendations – is transitioning towards the implementation of an HPV-based screening program. The incidence of cervical cancer in Czechia decreased by 24% and mortality by 10% between 2011-2021, with over 40% of cases in 2021 identified in stage I. [22], while the screening participation has been gradually increasing to 56.3% in 2021[23]. For a cervical cancer screening program to be effective high rate of participation is critical, with rates around 70% as proposed by the WHO [3] being considered sufficient to achieve significant reductions in cervical cancer burden [7, 14, 24, 25]. Key to increasing participation, is to analyze factors that motivate women to participate and factors that hinder their participation in cervical cancer screening. The Champion’s Health Belief Model Scale (CHBMS) [26] is a tool that allows for such analyses and allows to measure beliefs and attitudes that influence women's participation in breast cancer screening. While it was originally developed for participation in breast cancer screening [27], it has been previously adopted and validated to be used to evaluate participation in cervical cancer screening programs [28, 29]. The aim of this study was to use the CHBMS to measure the beliefs and attitudes influencing participation of eligible women in Czechia in the national cervical cancer screening program and to analyze the potential increase in motivation to participate if a home-based HPV-self test would be available. Methods Study design and population A cross-sectional population-based study was conducted on a representative sample of women eligible for cervical cancer screening in the Czech Republic in March-April 2025. Overall, 1500 women were recruited for the survey using a quota sampling strategy to ensure representativeness of the population of the Czech Republic for age, region of residence and size of town. The following exclusion criteria were applied in order to include only a sample of women eligible for cervical cancer screening: diagnosed cervical cancer, performed hysterectomy, age below 23 or over 64 years, and not being a citizen to the Czech Republic. Data collection The Champions Health Belief Model (CHBM)[26, 27] was used as a primary tool to collect data for our study. The CHBM is a validated instrument that is based on the Health Belief Model, designed to assess beliefs about health behaviors, often used in relation to cancer screening. In our study a version of the CHBM was used that was specifically adopted for use in relation to cervical cancer screening [28, 29], evaluating five core constructs: perceived susceptibility (measuring an individual’s beliefs about their personal risk or vulnerability to developing cervical cancer) perceived seriousness (measures beliefs about the seriousness or consequences of cervical cancer if it occurs) perceived benefits (measures the perceived positive outcomes of engaging in the recommended health behavior (e.g., cervical cancer screening) perceived barriers (measures the perceived obstacles or negative aspects associated with performing cervical cancer screening) self-efficacy (measures the individual’s confidence in their ability to successfully participate cervical cancer screening) Each of the five core domains contained additional subdomains. In addition to these five domains, an additional domain with four sub-domains was added that focused on the beliefs and attitudes about using a self-administered at-home HPV test over the current method of cervical cancer screening that involves a pap smear performed by a gynecologist. Before application, the tool translated from its original English version, and back-translated by two independent researchers which yielded the final translation that was piloted on a sample of 30 women fulfilling the eligibility criteria for the study. A five-point Likert scale was used to rate each subdomain (1 being the least agreeable and 5 denoting the highest level of agreement). In addition, a section on demographic and other health related characteristics of each survey participant was added. Please see the complete tool in Supplementary Table 2. The data collection was performed by a licensed market research agency that was procured for the purposes of this study. Of the 1500 respondents, 1200 were interviewed using online panels and 300 using telephone interviews. Analysis strategy and statistics The study follows two general lines of analysis. First, the domains of the CHBM were analyzed in association with previous screening behavior. Here, the association of the scores in each of the five domains of the CHBM and previous participation of each respondent in a cervical cancer screening was analyzed. Univariate analysis was used to explore the differences in the CHBM domains, demographic and health related characteristics of women who did or did not previously participate in cervical cancer screening. In the next step, logistic regression was used to analyze the association of the CHBM domains with previous cervical cancer screening participation while adjusting for the roles of all CHBM domains (model 1) and further adjusting for age, education and household income – factors that were previously shown to be associated with cervical cancer screening participation. [30-36] Secondly, the increase in motivation to participate in cervical cancer screening in case of availability of a self-administered and home-based HPV test, as compared to pap smear performed at the gynecologist was analyzed in association of additional factors pertaining to HPV self-testing and other demographic and health related characteristics of the respondents. First, univariate analyses comparing the characteristics of women who would and who would not be increasingly motivated by the availability of the HPV self-test were compared. After this, two logistic regression models were constructed – model 1 used additional factors pertaining to HPV self-testing as covariates adjusted for each other, and model 2 in addition adjusted factors that were significantly different between the two groups in the univariate analysis (model 2). To assess the reliability and sensitivity of the CHBM, Cronbach’s alpha along with ceiling and floor effects for each domain were calculated. Cronbach’s alpha is a measure of internal consistency that indicates how closely related a set of items are as a group (values above 0.70 are generally considered to reflect adequate reliability). Ceiling effect refers to the proportion of participants who achieved the highest possible score on a subscale, suggesting limited ability to detect improvement or change at the high end of the scale. Floor effect, conversely, indicates the proportion of participants who scored at the lowest possible value, which may reflect limited sensitivity at the low end. High ceiling or floor effects (typically above 15%) can reduce the instrument’s ability to discriminate between individuals. These analyses allowed us to evaluate the scale's reliability and its ability to capture a range of responses in our study population. The Chi-squared test was used to test for differences between two compared groups in case of categorical variables, and the two sample T-test was used to test for differences in continuous variables. Odds ratios with corresponding 95% confidence intervals were calculated to show the results of logistic regressions. A p value of <0.05 was considered statistically significant. The R statistical language was used for all analyses presented in this paper. Ethics committee Our research was conducted in accordance with the Declaration of Helsinki, the ethical approval for the study was granted by the Ethics Committee of the Faculty of Health Sciences and Social Work at Trnava University in Trnava, reference number EK-2/2K/2025. Results CHMB domains and screening participation Overall, 1500 women participated in the survey. The mean age was 43.6 years. Of the three high level regions, Czechia was represented the most and about 2/3 of the women were from towns with 5000 or more inhabitants. Only 4% did not complete middle school or higher education, and the majority were employees. More than 80% of them lived at the time of the survey or previously in a marriage or other type of partnership and nearly ¾ had children. Over half of the respondents (54%) reported a personal income between 20,000 – 40,000 CZK (equivalent to about 800-1600 EUR), and 29% below 20,000 CZK (800 EUR). On household level, half of the women estimated their income between 40,000-80,000 CZK (1600-3200 EUR). Most of them reported no previous HPV infection and about ¾ of them were not vaccinated against HPV. About 28% of the women had a chronic disease diagnosis at the time of the survey (E.g., cardiovascular disease, diabetes or other chronic non-communicable disease) and 102 women reported a history of cervical cancer in their family. Please see Supplementary table 1 for more details. From the whole sample, 1301 women answered the question whether they have participated in a cervical cancer screening before. Of these, 793 (61%) reported that they have previously participated in such screening. Table 1 summarizes and compares the key characteristics of both groups. Women who participated in cervical cancer screening were significantly older (mean age 45.4 vs 41.1 years), they had higher level of completed education, were more often married or living in a stable partnership and had children more often. The association between education and screening was notable, with university-educated women more likely to have participated, and those with only elementary education significantly less. Women who screened previously for cervical cancer also reported a diagnosed chronic illness more often, and more often engaged in breast or colorectal cancer screening before. In addition, they had higher reported rates of complete HPV vaccination and prior HPV infection, though overall vaccination rates remained relatively low. There were no significant differences between the groups in terms of sociodemographic variables such as region, town population or personal income, while household income was higher among screened women. Table 1: Demographic and health related characteristics of women by cervical cancer screening status Characteristic Screened n=793 (61%) Not Screened n=508 (39%) Total n=1301 P value Age (mean, 95%CI) 45.4 (44.7-46.2) 41.1 (40.1-42.2) 43.8 (43.1-44.4) <0.001 Age Categories (n, %) 23-34 years 135 (17%) 166 (33%) 301 (23%) <0.001 35-54 years 451 (57%) 251 (49%) 702 (54%) 55-64 years 207 (26%) 91 (18%) 298 (23%) Region (n, %) Prague 106 (13%) 52 (10%) 158 (12%) 0.24 Czechia 389 (49%) 257 (51%) 646 (50%) Moravia 298 (38%) 199 (39%) 497 (38%) Town population (n, %) <5000 inhabitants 263 (33%) 166 (33%) 429 (33%) 0.903 5000 or more 530 (66%) 342 (67%) 872 (67%) Education (n, %) Elementary school 21 (3%) 34 (7%) 55 (4%) <0.001 Middle school 514 (65%) 349 (69%) 863 (66%) University 258 (33%) 125 (25%) 383 (29%) Occupation (n, %) Employee 531 (67%) 326 (64%) 857 (66%) <0.001 Entrepreneur 56 (7%) 32 (6%) 88 (7%) Student 10 (1%) 26 (5%) 36 (3%) Maternal 79 (10%) 48 (9%) 127 (10%) Retired (medical grounds) 42 (5%) 16 (3%) 58 (5%) Retired 17 (2%) 7 (1%) 24 (2%) Unemployed 40 (5%) 44 (9%) 84 (7%) Other 18 (2%) 9 (2%) 27 (2%) Marital status (n, %) Single 113 (14%) 140 (28%) 253 (19%) <0.001 Partnership 205 (26%) 142 (28%) 347 (27%) Married 311 (39%) 138 (27%) 449 (35%) Divorced 152 (19%) 82 (16%) 234 (18%) Widow 12 (6%) 6 (1%) 18 (1%) Children (n, % Yes) 620 (78%) 173 (22%) 941 (72%) <0.001 Number of children (mean, 95%CI) 1.9 (1.8-2) 1.9 (1.8-2) 1.9 (1.8-1.9%) 0.459 Personal income (n, %) 40,000 78 (10%) 40 (8%) 118 (9%) No answer 68 (9%) 55 (11%) 123 (10%) Household income (n, %) <40,000 202 (26%) 166 (33%) 368 (28%) 80,000 91 (10%) 50 (10%) 141 (11%) No answer 76 (10%) 61 (12%) 137 (11%) Prior HPV infection (n, %) Yes 74 (9%) 11 (2%) 85 (7%) <0.001 No 653 (82%) 453 (89%) 1106 (85%) Unknown 66 (8%) 44 (9%) 110 (9%) HPV vaccination (n, %) Yes, both doses 104 (131%) 67 (13%) 171 (13%) <0.001 Yes, incomplete 7 (1%) 18 (4%) 25 (2%) No 635 (80%) 366 (72%) 1001 (77%) Unknown 47 (6%) 57 (11%) 104 (8%) Cervical cancer in family (n, %) Yes 55 (7%) 37 (7%) 92 (7%) 0.099 No 679 (86%) 416 (82%) 1095 (84%) Unknown 59 (7%) 55 (11%) 114 (9%) Chronic diseases (n, %) Yes 254 (32%) 101 (20%) 355 (27%) <0.001 No 508 (64%) 389 (77%) 897 (69%) Unknown 31 (4%) 18 (4%) 49 (4%) Prior Breast Cancer Screening (n, %) Yes 341 (82%) 94 (51%) 435 (72%) <0.001 No 59 (14%) 89 (48%) 148 (25%) Unknown 18 (4%) 3 (2%) 21 (4%) Prior Colorectal Cancer Screening (n, %) Yes 153 (52%) 23 (17%) 176 (41%) <0.001 No 128 (43%) 108 (79%) 236 (55%) Unknown 16 (5%) 5 (4%) 21 (5%) In further, we have used the CHBM to analyze the factors influencing participation in cervical cancer screening among eligible women in the Czech Republic. Table 2 presents the summary of our findings. We found statistically significant differences in the domains of benefits, barriers, and self-efficacy: screened women perceive greater benefits (mean score 4.16 vs. 3.69), fewer barriers (mean score 1.94 vs. 2.49), and higher self-efficacy (mean score 4.22 vs. 3.68), than their counterparts with no previous participation in cervical cancer screening. It is likely that these domains were the principal drivers of their participation. On the other hand, the differences in the domains of susceptibility, and severity suggesting that perceptions of risk and disease seriousness did not significantly influence participation on screening. The largest absolute difference in scores was found in the domain of barriers (difference of 0.55 points: 2.49 vs. 1.94), suggesting that perceived obstacles (e.g., logistical, emotional, or cultural) were the strongest factor driving non-participation in cervical cancer screening. Table 2: CHBM Domain scores by cervical screening status CHBM Domain Screened n=793 Not Screened n=508 Total n=1301 P value Susceptibility 2.38 (2.32-2.44) 2.3 (2.23-2.38) 2.36 (2.32-2.4) 0.129 Severity 3.13 (3.08-3.19) 3.14 (3.07-3.22) 3.14 (3.11-3.19) 0.861 Benefits 4.16 (4.11-4.2) 3.69 (3.62-3.76) 3.96 (3.93-4) <0.001 Barriers 1.94 (1.89-1.98) 2.49 (2.42-2.55) 2.15 (2.12-2.19) <0.001 Self-Efficacy 4.22 (4.18-4.26) 3.68 (3.61-3.75) 4 (3.96-4.1) <0.001 The influence of these domains is further quantified in Table 3. Here we present two logistic regression models – model 1 uses the scores in the 5 CHBM domains adjusted for the effect of each other and model 2 further adjusts for the effect of age, education and household income (the three predictors that were significantly different in univariate analyses, as presented in Table 1). Even after adjusting for the latter, a one point increase of score in the benefit domain would significantly increase the odds of previous cervical cancer screening by a factor of 1.6, an increase of score in the barriers domain would decrease this odds by a factor of 0.4, and an increase of score in the self-efficacy domain would increase the odds of previous cervical cancer screening by a factor of 1.6. Thus, regardless of education, income, age and the influence of susceptibility and severity, these three domains remained a strong predictor of participation of women in cervical cancer screening. Table 3: Logistic regression models (odds ratios and 95% confidence intervals) of the association between CHBM domains and past participation in cervical cancer screening Predictor ( CHBM Domain) Model 1* Model 2** OR (95% CI) P-value OR (95% CI) P-value Susceptibility 1.2 1.05-1.43 0.016 1.3 1.1-1.5 <0.01 Severity 0.89 0.75-1.06 0.198 0.94 0.79-1.12 0.515 Benefits 1.7 1.4-2.1 <0.001 1.6 1.3-2 <0.001 Barriers 0.42 0.34-0.52 <0.001 0.4 0.32-0.51 <0.001 Self-Efficacy 1.6 1.3-2 <0.001 1.6 1.2-2 <0.001 Model parameters Nagelkerke R 2 =0.263 AUC=0.759 Nagelkerke R 2 =0.309 AUC=0.781 *Model includes only the scores of the CHBM domains as predictors **Model in addition to model 1 adjusted for age, education and household income Self-administered HPV tests and screening participation Out of the overall sample of 1,500 women, 29% reported that the availability of a self-administered, at-home HPV test would increase their motivation to participate in cervical cancer screening when compared to Pap smears done by their gynecologist. These women were significantly older (mean age 45.1 vs. 42.9 years,) and showed distinct sociodemographic characteristics: they were more often single or divorced compared to the group with no increase in motivation (44% vs. 35%) and a lower proportion of them had children (67% vs. 75%). Women who did report increased motivation were also less likely to have previously participated in breast cancer screening (68% vs. 73%) and colorectal cancer screening (37% vs 40%). HPV-related history (vaccination status, prior infection) and family history of cervical cancer were not significantly associated with increased motivation for self-testing. Socio-demographic factors like region, urbanization, income, and education showed no significant association with increased motivation. Please see Table 4 for details Table 4: Demographic and health related characteristics of women by increase in motivation to participate in cervical cancer screening if a self-administered HPV test is available Characteristic Increased motivation to screen n=427 (29%) No increase in motivation to screen n=1073 (71%) Total n=1500 P value Age (mean, 95%CI) 45.1 (44-46.3) 42.9 (42.3-43.6) 43.6 (43-44.1) <0.01 Age Categories (n, %) 23-34 years 95 (22%) 274 (26%) 369 (25%) 0.013 35-54 years 214 (50%) 577 (54%) 791 (53%) 55-64 years 118 (21%) 222 (21%) 340 (23%) Region (n, %) Prague 60 (14%) 131 (12%) 191 (13%) 0.509 Czechia 200 (47%) 532 (50%) 732 (49%) Moravia 167 (39%) 410 (38%) 577 (39%) Town population (n, %) <5000 inhabitants 130 (30%) 356 (33%) 486 (32%) 0.337 5000 or more 297 (70%) 717 (67%) 1014 (68%) Education (n, %) Elementary school 27 (6%) 40 (4%) 67 (5%) 0.086 Middle school 275 (64%) 718 (67%) 993 (66%) University 125 (29%) 315 (29%) 440 (29%) Occupation (n, %) Employee 286 (67%) 700 (65%) 986 (66%) <0.01 Entrepreneur 28 (7%) 65 (6%) 93 (6%) Student 14 (3%) 26 (2%) 40 (3%) Maternal 29 (7%) 126 (12%) 155 (10%) Retired (medical grounds) 20 (5%) 48 (5%) 68 (5%) Retired 9 (2%) 21 (2%) 30 (2%) Unemployed 24 (6%) 73 (7%) 97 (7%) Other 17 (4%) 14 (1%) 31 (2%) Marital status (n, %) Single 98 (23%) 189 (18%) 287 (19%) <0.01 Partnership 100 (23%) 299 (28%) 399 (27%) Married 130 (30%) 387 (36%) 517 (35%) Divorced 88 (21%) 186 (17%) 274 (18%) Widow 11 (3%) 12 (1%) 23 (2%) Children (n, % Yes) 286 (67%) 799 (75%) 7085 (72%) <0.01 Number of children (mean, 95%CI) 1.96 (1.86-2.07) 1.86 (1.8-1.92) 1.89 (1.83-1.93) 0.093 Personal income (n, %) 40,000 44 (10%) 91 (9%) 135 (9%) No answer 34 (8%) 119 (11%) 153 (10%) Household income (n, %) 80,000 47 (11%) 112 (10%) 159 (11%) No answer 38 (9%) 130 (12%) 168 (11%) Prior HPV infection (n, %) Yes 18 (4%) 72 (7%) 90 (6%) 0.176 No 368 (86%) 895 (83%) 1263 (84%) Unknown 41 (10%) 106 (10%) 147 (10%) HPV vaccination (n, %) Yes, both doses 56 (13%) 136 (13%) 192 (13%) 0.469 Yes, incomplete 14 (3%) 21 (2%) 35 (2%) No 319 (75%) 813 (76%) 1132 (76%) Unknown 38 (103%) 103 (10%) 141 (9%) Cervical cancer in family (n, %) Yes 31 (7%) 71 (7%) 102 (7%) 0.857 No 353 (83%) 887 (83%) 1240 (83%) Unknown 43 (11%) 115 (11%) 158 (11%) Chronic diseases (n, %) Yes 127 (30%) 296 (28%) 423 (28%) 0.689 No 281 (66%) 725 (68%) 1006 (67%) Unknown 19 (4%) 52 (5%) 71 (5%) Prior Breast Cancer Screening (n, %) Yes 161 (68%) 329 (73%) 490 (71%) <0.001 No 72 (31%) 93 (21%) 162 (24%) Unknown 3 (1%) 30 (7%) 33 (5%) Prior Colorectal Cancer Screening (n, %) Yes 67 (37%) 127 (40%) 194 (39%) 0.014 No 107 (60%) 162 (51%) 269 (54%) Unknown 5 (3%) 29 (9%) 34 (7%) In order to further elucidate the factors influencing the increase in motivation to screen if self-administered, at-home HPV test would be available, additional questions from the survey pertaining to HPV self-testing were analyzed. These findings are summarized in Table 5. Women who reported increased motivation to participate screening if a self-administered HPV test were available reported significantly higher willingness to use such a test compared to those who reported no change in motivation (mean score 4.28 vs. 3.32). Similarly, the role of health insurance coverage as a motivator was higher in this group (mean score of 4.2 vs. 3.36). These findings suggest that perceived convenience and affordability are key facilitators of self-test acceptance, particularly among women already inclined to increase their screening participation. Women who reported an increased motivation to screen were significantly less concerned about performing the self-test incorrectly (mean score 3.25 vs. 3.64). However, when asked about their willingness to seek follow-up care with a gynecologist if the self-test returned a positive result, the pattern was reversed: women in the non-motivated group showed higher willingness (mean score 3.64 vs. 3.25). This could reflect a greater trust in the conventional medical pathway among those less inclined to adopt self-testing. Table 5: Scores of selected factors pertaining to HPV self-test used to screen for cervical cancer by increase in motivation to participate in cervical cancer screening if a self-administered HPV test is available Item Increased motivation to screen n=427 (29%) No increase in motivation to screen n=1073 (71%) Total n=1500 P value Willingness to use self-test (mean, 95%CI) 4.28 (4.21-4.33) 3.32 (3.26-3.39) 3.6 (3.54-3.65) <0.001 Increased willingness if covered by health insurance (mean, 95%CI) 4.2 (4.12-4.27) 3.36 (3.3-3.43) 3.61 (3.55-3.66) <0.001 Fear of test misuse (mean, 95%CI) 3.25 (3.15-3.35) 3.64 (3.58-3.7) 3.53 (3.48-3.58) <0.001 Willingness to follow up with physician after self-test (mean, 95%CI) 3.25 (3.15-3.35) 3.64 (3.58-3.7) 3.53 (3.47-3.58) <0.001 Table 6 presents the results of logistic regression analyses of the association of increase in motivation to screen if self-administered, at-home HPV test would be available and further factors pertaining to HPV self-testing as predictors. Two models were constructed, model 1 contains the four domains as covariates and model 2 further adjusts for age, occupation, marital status and having children (E.g., the four factors that were significantly different in the univariate analyses shown in table 4). In both logistic regression models, the strongest predictor of increased motivation was the willingness to use a self-test (OR=5.8, 95% CI: 4.3–8.1 in the adjusted model). The perception that insurance coverage would increase willingness was also significantly associated with motivation (adjusted OR=2.0, 95% CI: 1.6–2.5). On the other hand, fear of test misuse was associated with a lower likelihood of increased motivation (OR=0.65, 95% CI: 0.55–0.77), similarly to willingness to follow up with a physician after a positive self-test (OR=0.46, 95% CI: 0.36–0.59), suggesting that those already inclined to follow up may not see self-testing as increasing their motivation. Table 6: Logistic regression models of the association between selected factors pertaining to HPV self-test and the increase in motivation to participate in cervical cancer screening if a self-administered HPV test is available Predictor Model 1 Model 2 OR (95% CI) P-value OR (95% CI) P-value Willingness to use self-test 5.9 (4.4-8) <0.001 5.8 (4.3-8.1) <0.001 Increased willingness if covered by health insurance 1.9 (1.5-2.4) <0.001 2.0 (1.6-2.5) <0.001 Fear of test misuse 0.63 (0.54-0.75) <0.001 0.65 (0.55-0.77) <0.001 Willingness to follow up with physician after self-test 0.46 (0.35-0.58) <0.001 0.46 (0.36-0.59) <0.001 Model parameters Nagelkerke R 2 =0.414 AUC=0.848 Nagelkerke R 2 =0.442 AUC=0.860 *Model includes only the scores of the selected factors pertaining to HPV self-test as predictors **Model in addition to model 1 adjusted for age, occupation, marital status and having children Table 7 presents the reliability and internal validity of the CHBMS domains used in this study. All domains demonstrated high reliability, with Cronbach’s alpha values ranging from 0.87 (Benefits) to 0.95 (Self-Efficacy), indicating strong internal consistency. Ceiling effects were low across most domains (0.5%–1.8%), except for Benefits (13.3%) and Self-Efficacy (11.8%), suggesting slightly higher frequencies of answers on the higher end of the scale in these domains. Floor effects were minimal (0.6%–2.6%), except for Susceptibility (17%, 95% CI: 15%–19%), indicating limited clustering of answers a at the lower end of the scale. In summary, these parameters confirm the CHBM tool’s robust psychometric properties for assessing cervical cancer screening beliefs in this population. Table 7: Reliability and validity of the CHBM domains used in the survey: Cronbach’s Alpha with 95% CI, Ceiling Effect and Floor Effect with 95%CI CHBM Domain Cronbach’s Alpha (95%CI) Ceiling Effect (95%CI) Floor Effect (95%CI) Susceptibility 0.93 (0.93-0.94) 0.6% (0.3%-1.2%) 17% (15%-19%) Severity 0.88 (0.87-0.89) 1.8% (1.2%-2.6%) 2.6% (1.9%-3.6%) Benefits 0.87 (0.85-0.88) 13.3% (11.6%-25.3%) 0.6% (0.3%-1.2%) Barriers 0.93 (0.92-0.93) 0.5% (0.3%-1.1%) 2.5% (1.8%-3.5%) Self-Efficacy 0.95 (0.95-0.95) 11.8% (10.2%-13.7%) 0.9% (0.5%-1.5%) Discussion We conducted a nationally representative survey using the CHBMS to identify predictors of cervical cancer screening participation among eligible women in Czechia. In addition, we explored the potential of self-administered HPV testing to enhance screening motivation and participation. We found that 61% of eligible women reported prior participation in cervical cancer screening. Their participation was strongly predicted by perceived benefits (OR 1.6, 95%CI:1.3-2), self-efficacy (OR 1.6, 95%CI: 1.2-2) and perceived barriers (OR 0.4, 95%CI:0.32-0.51) even after adjusting for age, education and income. The availability of a home-based self-administered HPV test instead of a cytological exam would increase the motivation to participate in the screening program in 29% of the eligible women. This motivation is strongly predicted by the willingness of women to the self-test (OR 5.8, 95%CI:4.3-8.1) and covering the cost by health insurance (OR 2. 95%CI: 1.6-2.5), while fear of test misuse and the willingness to follow-up with the physician after the self-test reduced significantly this motivation – OR 0.65 (95%CI: 0.55-0.77) and 0.46 (95%CI: 0.36-0.59), respectively. The proportion of women who previously participated in cervical screening found in our study was 61%, which is in line with the participation reported by national authorities (56,2 % screened among the 25–59 year old women in 2021) [21-23], but still lags behind the 70% threshold outlined by the WHO [3] and suggested by evidence to be a critical participation rate for an effective cervical cancer screening program [7, 14, 24, 25]. In terms of demographic characteristics, those that participated previously in cervical cancer screening tended to be older (mean 45.4 vs. 41.1 years in non-participants), had higher education (33% university-educated vs. 25%), were married or in a partnership (65% vs. 55%), had children more often (78% vs. 22%) and higher household income. Previous studies similarly showed higher participation among women with higher attained education [30, 32] - especially in early stages of population-based screening programs [36], among those with higher income [34], higher age [25, 35], and among un-married women [31, 33]. Thus, demographically, our findings are well aligned with the current literature. We found that the participation was driven mainly by the benefits, barriers, and self-efficacy domains of the CHBM. These findings suggest that women who see strong, concrete positive outcomes from cervical cancer screening, and those who believe that they are able to arrange and complete their screening successfully are significantly more likely to participate. These findings are confirmed by a large-scale systematic review that recognized the awareness of screening benefits and the confidence (self-efficacy) of women in their ability to successfully go through the screening procedures as principal conducive factors for participation [37]. These domains strongly predicted cervical cancer screening participation also in other studies that applied the HBM model [38-40]. The susceptibility domain (E.g., the perception of risk of getting cervical cancer) had a relatively less strong, but significant association with cancer participation (OR 1.3, 95%CI:1.1-1.5) – the pattern confirming similar findings of published studies [32, 37, 41]. On the other hand, higher scores of perceived barriers were strong predictors or non-participation in screening in our study, suggesting that practical, emotional, cultural/personal or health system/structural obstacles are a major hindering factor for participation. This is widely confirmed by previous studies in various populations [32, 37-41]. In contrast, perceived severity was not significantly associated with cervical cancer screening participation, which may reflect lower awareness of the seriousness of the disease in the population – further studies are needed to elucidate this finding. While the substantial hindering effect of perceived barriers can be seen as a problem, it also may be an opportunity to increase the participation by redesigning the screening in a way that overcomes some of these barriers. Introduction of HPV-test based screening may be an important milestone and Czechia is in the process of transitioning towards this method of screening [21, 35]. Our study found that nearly a third (29%) of eligible women would be more likely to participate in the screening if a home-based HPV-test would be available (as opposed to a cytological exam). Compared to those that would not be more motivated, they were older (mean age 45.1 vs. 42.9 years), more likely to be single or divorced (44% vs. 35%), less likely to have children (67% vs. 75%), and also less likely to participate in breast or colorectal cancer screening. These results suggest that self-administered HPV testing could address key barriers to screening, particularly for women who find clinic-based Pap smears inconvenient or uncomfortable. Findings from published studies report that HPV self-sampling increased the number of non-attenders screened [42], and suggest that it is a far superior way to reach under-screened populations compared to mail-in invitations [43, 44]. Furthermore, this method has been successfully piloted in Czechia [45] and Slovakia (a country with similar social and cultural background) [46], which supports its feasibility on scale. However, there are issues that needs to be addressed: while we found that among eligible women in Czechia willingness of the use of an HPV self-test and coverage of cost by health insurance are strongly associated with their increased motivation to participate, willingness to follow up with physicians if needed may hinder their participation. Thus, without robust follow-up systems, the benefits of increased screening participation could be undermined by missed diagnoses or delayed treatment. Czechia’s ongoing transition to HPV-based screening at ages 35 and 45 provides an opportunity to integrate self-sampling with clear follow-up pathways, such as automated reminders or telehealth consultations, to address this challenge. Some limitations should be considered when interpreting these findings. First, the study relies entirely on self-reported data and this may be subject to recall bias. Second, the use of online and telephone surveys may have underrepresented women with limited digital access, potentially introducing selection bias. Therefore, when interpreting and generalizing the findings of this study, these factors should be observed. However, the fact the participation rate found in our study is largely in line with official data released by national authorities in Czechia suggests that the sampling of the study was robust and produced a representative and valid dataset. Furthermore, a validated and established tool has been used to collect data on health beliefs which further supports the validity of our findings. Conclusion This study highlights the critical role of perceived barriers, benefits, and self-efficacy in driving cervical cancer screening participation in Czechia. The potential of self-administered HPV testing to increase motivation among underserved groups offers a promising strategy to enhance screening coverage. By addressing barriers, promoting benefits, and ensuring robust follow-up systems, Czechia can make significant progress toward meeting the WHO’s 70% coverage target and reducing its cervical cancer burden. These findings provide a foundation for targeted public health interventions and future research to advance cervical cancer prevention in the region. Declarations Competing interests The authors declare no conflict of interest. Ethics approval Our research was conducted in accordance with the Declaration of Helsinki, the ethical approval for the study was granted by the Ethics Committee of the Faculty of Health Sciences and Social Work at Trnava University in Trnava, reference number EK-2/2K/2025. Consent to participate Informed consent was obtained electronically from all participants prior to initiating the online questionnaire. At the outset of the survey, each participant was presented with a detailed information sheet and a consent form outlining the purpose of the study, data handling procedures, and their rights. Only those who actively provided consent were granted access to the questionnaire; without this confirmation, progression to the survey items was not possible. Funding statement This research was funded by Slovakia's plan for resilience and recovery, grant 09I01-03-V04-00058 and by the Institutional Support for the Long‑term Conceptual Development of a Research Organization, provided by the Ministry of Education, Youth and Sports of the Czech Republic. Author Contribution MM, KJ, JF conceived the study and designed the research. Data collection was undertaken by KJ, JF, DM, DP, JM and PS. MM, DP, DM, and KJ performed the data analysis with insights from JM, KP, and JF. KJ and MM drafted the initial manuscript, while all other authors critically revised it for intellectual content. 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16:05:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":759370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7719792/v1/ce510f39-573f-470c-8eba-e61c3d2f74df.pdf"},{"id":94993990,"identity":"f5e986bf-0ed7-43ce-9095-4ec1fa957bc8","added_by":"auto","created_at":"2025-11-03 07:37:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21503,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7719792/v1/6c4245d69f6b2631957e3a37.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Health Beliefs and HPV Self-Testing in Cervical Cancer Screening Participation in Czechia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer poses a\u0026nbsp;serious public health and societal problem globally. According to the Global Cancer Observatory (GLOBOCAN), as of 2022, cervical cancer ranks as 8\u003csup\u003eth\u003c/sup\u003e in terms of incidence (with a global age standardized incidence rate of 14.1 per 100,000 and over 660,000 new cases annually) and 9\u003csup\u003eth\u003c/sup\u003e in terms of mortality (with an annual mortality of 7.1 which translates into more than 348,000 annual deaths) [1]. While in Europe, the annual number of new cases is projected to decrease by 4.4% from 2022 to 2045 (attributable to decrease in population size), in other regions substantial increases are projected that are most apparent in low-income regions [2], confirming persisting global inequalities in incidence and mortality [3].\u003c/p\u003e\n\u003cp\u003eThanks to the well-established causal link between human papilloma virus infection and cervical cancer [4, 5], there are effective strategies that could help to overturn these unfavorable projections [2]. Key stakeholders acted in this regard. In 2020, the World Health Organization (WHO) adopted the Global strategy to accelerate the elimination of cervical cancer as a public health problem [3] \u0026ndash; an ambitious plan aiming to have 90% of girls vaccinated with HPV vaccine by age 15; 70% of women are screened with a high-performance test by 35 years and again by 45 years; and 90% of women identified with cervical disease receive treatment. Aligned with this strategy, Europe\u0026rsquo;s Beating Cancer Plan is a political commitment of the European Union (EU) to take action against cancer through ten flagship initiatives that include HPV vaccination and cervical cancer screening as key strategies to tackle cervical cancer in Europe [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, besides vaccination, screening has been established as a key strategy in fighting cervical cancer. A recent systematic review summarizing studies from European countries showed a reduction of mortality due to cervical cancer of 41% to 92% in women attending population-based screening, compared to those not attending [7]. A modelling study in six European countries showed a projected 50-60% drop in incidence rates for a period between 2017-2040 if a screening program is introduced vs. no screening program [8]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe two major types of screening are cytology-based (Pap Smear) screening and HPV-based screening. While cytology-based screening has been shown to be very effective \u0026ndash; a systematic review showed a decrease of incidence risk by over 60% [9], they are dependent on well-organized patient follow-up, additional diagnostics and the availability of patient management resources [3]. Their implementation is therefore problematic, especially in low- and middle-income countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, HPV-based screening provides clear added benefits over cytology: randomized trials showed that they provide 60\u0026ndash;70% greater protection against invasive cervical carcinomas compared with cytology [10]; they have superior specificity, their strong negative predictive value allows for prolonged retest period [3]; and they are cost-effective under most scenarios in European settings [11] and countries outside Europe [12, 13]. In addition, HPV-based screening can be provided as a home-based self-test that can provide samples of similar quality as clinician samples and can increase participation of women in the screening, compared to invitations-based approaches [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt has been estimated that the implementation of HPV-based screening at age 35 and repeated at age 45 years with 70% coverage (as targeted by the WHO) globally, could avert between 12.5\u0026ndash;13.4 millions of cervical cancer cases in the next 50 years [15]. Based on these facts, HPV-based screening is the method of choice that is advocated by WHO [3] and many countries of Europe are transitioning from cytology-based screening as the primary population-based screening method, with the Netherlands already fully transitioned [16].\u003c/p\u003e\n\u003cp\u003eDespite the evidence that shows their many benefits, countries are lagging in introducing population based cervical cancer screening programs, which is especially apparent in low- and middle-income countries (who)[3]. While by 2020 a population screening program for cervical cancer was introduced in 22 EU member states, in some countries they are still not fully implemented and large inequalities are observed in uptake ranging from 25% to 80% [6, 17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Czechia - while pap smears have been in use since the 1960\u0026rsquo;s [18] \u0026ndash; a national based screening program using the method was only rolled out in 2008 [18-20]. In 2021 it was supplemented by an HPV test done at the time of the Pap smear at ages 35, 45 and since 2024 also at age of 55 years [21]. \u0026nbsp;Thus, the country \u0026ndash; in line with the evidence and recommendations \u0026ndash; is transitioning towards the implementation of an HPV-based screening program. The incidence of cervical cancer in Czechia decreased by 24% and mortality by 10% between 2011-2021, with over 40% of cases in 2021 identified in stage I. [22], while the screening participation has been gradually increasing to 56.3% in 2021[23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor a cervical cancer screening program to be effective high rate of participation is critical, with rates around 70% as proposed by the WHO [3] being considered sufficient to achieve significant reductions in cervical cancer burden [7, 14, 24, 25]. Key to increasing participation, is to analyze factors that motivate women to participate and factors that hinder their participation in cervical cancer screening. The Champion\u0026rsquo;s Health Belief Model Scale (CHBMS) [26] is a tool that allows for such analyses and allows to measure beliefs and attitudes that influence women\u0026apos;s participation in breast cancer screening. While it was originally developed for participation in breast cancer screening [27], it has been previously adopted and validated to be used to evaluate participation in cervical cancer screening programs [28, 29].\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to use the CHBMS to measure the beliefs and attitudes influencing participation of eligible women in Czechia in the national cervical cancer screening program and to analyze the potential increase in motivation to participate if a home-based HPV-self test would be available.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design and population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional population-based study was conducted on a representative sample of women eligible for cervical cancer screening in the Czech Republic in March-April 2025. Overall, 1500 women were recruited for the survey using a quota sampling strategy to ensure representativeness of the population of the Czech Republic for age, region of residence and size of town. The following exclusion criteria were applied in order to include only a sample of women eligible for cervical cancer screening: diagnosed cervical cancer, performed hysterectomy, age below 23 or over 64 years, and not being a citizen to the Czech Republic.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Champions Health Belief Model (CHBM)[26, 27] was used as a primary tool to collect data for our study. The CHBM is a validated instrument that is based on the Health Belief Model, designed to assess beliefs about health behaviors, often used in relation to cancer screening.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study a version of the CHBM was used that was specifically adopted for use in relation to cervical cancer screening [28, 29], evaluating five core constructs:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eperceived susceptibility (measuring an individual\u0026rsquo;s beliefs about their personal risk or vulnerability to developing cervical cancer)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eperceived seriousness (measures\u0026nbsp;beliefs about the seriousness or consequences of cervical cancer if it occurs)\u003c/li\u003e\n \u003cli\u003eperceived benefits (measures the perceived positive outcomes of engaging in the recommended health behavior (e.g., cervical cancer screening)\u003c/li\u003e\n \u003cli\u003eperceived barriers (measures the perceived obstacles or negative aspects associated with performing cervical cancer screening)\u003c/li\u003e\n \u003cli\u003eself-efficacy (measures the individual\u0026rsquo;s confidence in their ability to successfully participate cervical cancer screening)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEach of the five core domains contained additional subdomains. In addition to these five domains, an additional domain with four sub-domains was added that focused on the beliefs and attitudes about using a self-administered at-home HPV test over the current method of cervical cancer screening that involves a pap smear performed by a gynecologist. Before application, the tool translated from its original English version, and back-translated by two independent researchers which yielded the final translation that was piloted on a sample of 30 women fulfilling the eligibility criteria for the study.\u003c/p\u003e\n\u003cp\u003eA five-point Likert scale was used to rate each subdomain (1 being the least agreeable and 5 denoting the highest level of agreement). In addition, a section on demographic and other health related characteristics of each survey participant was added. Please see the complete tool in Supplementary Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data collection was performed by a licensed market research agency that was procured for the purposes of this study. Of the 1500 respondents, 1200 were interviewed using online panels and 300 using telephone interviews.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis strategy and statistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study follows two general lines of analysis. First, the domains of the CHBM were analyzed in association with previous screening behavior. Here, the association of the scores in each of the five domains of the CHBM and previous participation of each respondent in a cervical cancer screening was analyzed. Univariate analysis was used to explore the differences in the CHBM domains, demographic and health related characteristics of women who did or did not previously participate in cervical cancer screening. In the next step, logistic regression was used to analyze the association of the CHBM domains with previous cervical cancer screening participation while adjusting for the roles of all CHBM domains (model 1) and further adjusting for age, education and household income \u0026ndash; factors that were previously shown to be associated with cervical cancer screening participation. [30-36]\u003c/p\u003e\n\u003cp\u003eSecondly, the increase in motivation to participate in cervical cancer screening in case of availability of a self-administered and home-based HPV test, as compared to pap smear performed at the gynecologist was analyzed in association of additional factors pertaining to HPV self-testing and other demographic and health related characteristics of the respondents. First, univariate analyses comparing the characteristics of women who would and who would not be increasingly motivated by the availability of the HPV self-test were compared. After this, two logistic regression models were constructed \u0026ndash; model 1 used additional factors pertaining to HPV self-testing as covariates adjusted for each other, and model 2 in addition adjusted factors that were significantly different between the two groups in the univariate analysis (model 2).\u003c/p\u003e\n\u003cp\u003eTo assess the reliability and sensitivity of the CHBM, Cronbach\u0026rsquo;s alpha along with ceiling and floor effects for each domain were calculated. Cronbach\u0026rsquo;s alpha is a measure of internal consistency that indicates how closely related a set of items are as a group (values above 0.70 are generally considered to reflect adequate reliability). Ceiling effect refers to the proportion of participants who achieved the highest possible score on a subscale, suggesting limited ability to detect improvement or change at the high end of the scale. Floor effect, conversely, indicates the proportion of participants who scored at the lowest possible value, which may reflect limited sensitivity at the low end. High ceiling or floor effects (typically above 15%) can reduce the instrument\u0026rsquo;s ability to discriminate between individuals. These analyses allowed us to evaluate the scale\u0026apos;s reliability and its ability to capture a range of responses in our study population.\u003c/p\u003e\n\u003cp\u003eThe Chi-squared test was used to test for differences between two compared groups in case of categorical variables, and the two sample T-test was used to test for differences in continuous variables. Odds ratios with corresponding 95% confidence intervals were calculated to show the results of logistic regressions. A p value of \u0026lt;0.05 was considered statistically significant. The R statistical language was used for all analyses presented in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics committee\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur research was conducted in accordance with the Declaration of Helsinki, the ethical approval for the study was granted by the Ethics Committee of the Faculty of Health Sciences and Social Work at Trnava University in Trnava, reference number EK-2/2K/2025.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eCHMB domains and screening participation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 1500 women participated in the survey. The mean age was 43.6 years. Of the three high level regions, Czechia was represented the most and about 2/3 of the women were from towns with 5000 or more inhabitants. Only 4% did not complete middle school or higher education, and the majority were employees. More than 80% of them lived at the time of the survey or previously in a marriage or other type of partnership and nearly \u0026frac34; had children. Over half of the respondents (54%) reported a personal income between 20,000 \u0026ndash; 40,000 CZK (equivalent to about 800-1600 EUR), and 29% below 20,000 CZK (800 EUR). On household level, half of the women estimated their income between 40,000-80,000 CZK (1600-3200 EUR). Most of them reported no previous HPV infection and about \u0026frac34; of them were not vaccinated against HPV. About 28% of the women had a chronic disease diagnosis at the time of the survey (E.g., cardiovascular disease, diabetes or other chronic non-communicable disease) and 102 women reported a history of cervical cancer in their family. Please see Supplementary table 1 for more details.\u003c/p\u003e\n\u003cp\u003eFrom the whole sample, 1301 women answered the question whether they have participated in a cervical cancer screening before. Of these, 793 (61%) reported that they have previously participated in such screening. Table 1 summarizes and compares the key characteristics of both groups. Women who participated in cervical cancer screening were significantly older (mean age 45.4 vs 41.1 years), they had higher level of completed education, were more often married or living in a stable partnership and had children more often. The association between education and screening was notable, with university-educated women more likely to have participated, and those with only elementary education significantly less. Women who screened previously for cervical cancer also reported a diagnosed chronic illness more often, and more often engaged in breast or colorectal cancer screening before. In addition, they had higher reported rates of complete HPV vaccination and prior HPV infection, though overall vaccination rates remained relatively low. There were no significant differences between the groups in terms of sociodemographic variables such as region, town population or personal income, while household income was higher among screened women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Demographic and health related characteristics of women by cervical cancer screening status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreened\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=793 (61%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Screened\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=508 (39%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=1301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e(mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e45.4 (44.7-46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e41.1 (40.1-42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e43.8 (43.1-44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Categories\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e23-34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e135 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e166 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e301 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e35-54 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e451 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e251 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e702 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e55-64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e207 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e91 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e298 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u0026nbsp;\u003c/strong\u003e(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003ePrague\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e106 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e52 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e158 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eCzechia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e389 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e257 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e646 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eMoravia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e298 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e199 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e497 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTown population\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u0026lt;5000 inhabitants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e263 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e166 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e429 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e5000 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e530 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e342 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e872 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e21 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e34 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e55 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e514 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e349 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e863 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e258 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e125 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e383 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e531 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e326 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e857 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eEntrepreneur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e56 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e32 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e88 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e10 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e26 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e36 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eMaternal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e79 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e48 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e127 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eRetired (medical grounds)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e42 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e16 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e58 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e17 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e7 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e24 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e40 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e44 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e84 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e18 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e9 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e27 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e113 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e140 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e253 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003ePartnership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e205 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e142 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e347 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e311 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e138 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e449 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e152 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e82 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e234 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eWidow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e12 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e6 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e18 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChildren\u003c/strong\u003e (n, % Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e620 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e173 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e941 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of children\u003c/strong\u003e (mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e1.9 (1.8-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e1.9 (1.8-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e1.9 (1.8-1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal income\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u0026lt;20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e213 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e140 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e353 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e20,000-30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e266 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e181 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e447 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e30,001-40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e168 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e92 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e260 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u0026gt;40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e78 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e40 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e118 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e68 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e55 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e123 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u0026lt;40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e202 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e166 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e368 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e40,000-60,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e244 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e150 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e394 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e60,001-80,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e180 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e81 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e261 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u0026gt;80,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e91 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e50 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e141 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e76 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e61 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e137 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior HPV infection\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e74 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e11 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e85 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e653 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e453 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e1106 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e66 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e44 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e110 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPV vaccination\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes, both doses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e104 (131%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e67 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e171 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes, incomplete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e7 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e18 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e25 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e635 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e366 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e1001 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e47 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e57 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e104 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCervical cancer in family\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e55 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e37 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e92 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e679 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e416 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e1095 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e59 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e55 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e114 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic diseases\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e254 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e101 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e355 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e508 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e389 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e897 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e31 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e18 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e49 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior Breast Cancer Screening\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e341 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e94 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e435 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e59 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e89 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e148 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e18 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e21 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior Colorectal Cancer Screening\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e153 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e23 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e176 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e128 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e108 (79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e236 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.2011%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2356%;\"\u003e\n \u003cp\u003e16 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6724%;\"\u003e\n \u003cp\u003e21 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2184%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn further, we have used the CHBM to analyze the factors influencing participation in cervical cancer screening among eligible women in the Czech Republic. Table 2 presents the summary of our findings. We found statistically significant differences in the domains of benefits, barriers, and self-efficacy: screened women perceive greater benefits (mean score 4.16 vs. 3.69), fewer barriers (mean score 1.94 vs. 2.49), and higher self-efficacy (mean score 4.22 vs. 3.68), than their counterparts with no previous participation in cervical cancer screening. It is likely that these domains were the principal drivers of their participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the differences in the domains of susceptibility, and severity suggesting that perceptions of risk and disease seriousness did not significantly influence participation on screening. The largest absolute difference in scores was found in the domain of barriers (difference of 0.55 points: 2.49 vs. 1.94), suggesting that perceived obstacles (e.g., logistical, emotional, or cultural) were the strongest factor driving non-participation in cervical cancer screening.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: CHBM Domain scores by cervical screening status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.7202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHBM\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreened\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Screened\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=508\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=1301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.7202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSusceptibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e2.38 (2.32-2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e2.3 (2.23-2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e2.36 (2.32-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.7202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e3.13 (3.08-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e3.14 (3.07-3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e3.14 (3.11-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.7202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e4.16 (4.11-4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e3.69 (3.62-3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e3.96 (3.93-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.7202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarriers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e1.94 (1.89-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e2.49 (2.42-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e2.15 (2.12-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.7202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Efficacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e4.22 (4.18-4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e3.68 (3.61-3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e4 (3.96-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3199%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe influence of these domains is further quantified in Table 3. Here we present two logistic regression models \u0026ndash; model 1 uses the scores in the 5 CHBM domains adjusted for the effect of each other and model 2 further adjusts for the effect of age, education and household income (the three predictors that were significantly different in univariate analyses, as presented in Table 1). Even after adjusting for the latter, a one point increase of score in the benefit domain would significantly increase the odds of previous cervical cancer screening by a factor of 1.6, \u0026nbsp;an increase of score in the barriers domain would decrease this odds by a factor of 0.4, and an increase of score in the self-efficacy domain would increase the odds of previous cervical cancer screening by a factor of 1.6. Thus, regardless of education, income, age and the influence of susceptibility and severity, these three domains remained a strong predictor of participation of women in cervical cancer screening.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Logistic regression models (odds ratios and 95% confidence intervals) of the association between CHBM domains and past participation in cervical cancer screening\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor (\u003c/strong\u003e\u003cstrong\u003eCHBM Domain)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSusceptibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.2 1.05-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.3 1.1-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.89 0.75-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.94 0.79-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.7 1.4-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.6 1.3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarriers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.42 0.34-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.4 0.32-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Efficacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.6 1.3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.6 1.2-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e=0.263\u003c/p\u003e\n \u003cp\u003eAUC=0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e=0.309\u003c/p\u003e\n \u003cp\u003eAUC=0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Model includes only the scores of the CHBM domains as predictors\u003c/p\u003e\n\u003cp\u003e**Model in addition to model 1 adjusted for age, education and household income\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSelf-administered HPV tests and screening participation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOut of the overall sample of 1,500 women, 29% reported that the availability of a self-administered, at-home HPV test would increase their motivation to participate in cervical cancer screening when compared to Pap smears done by their gynecologist. These women were significantly older (mean age 45.1 vs. 42.9 years,) and showed distinct sociodemographic characteristics: they were more often single or divorced compared to the group with no increase in motivation (44% vs. 35%) and a lower proportion of them had children (67% vs. 75%). Women who did report increased motivation were also less likely to have previously participated in breast cancer screening (68% vs. 73%) and colorectal cancer screening (37% vs 40%). HPV-related history (vaccination status, prior infection) and family history of cervical cancer were not significantly associated with increased motivation for self-testing. Socio-demographic factors like region, urbanization, income, and education showed no significant association with increased motivation. Please see Table 4 for details\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Demographic and health related characteristics of women by increase in motivation to participate in cervical cancer screening if a self-administered HPV test is available\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncreased motivation to screen\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=427 (29%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo increase in motivation to screen\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=1073 (71%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=1500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e(mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e45.1 (44-46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e42.9 (42.3-43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e43.6 (43-44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Categories\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e23-34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e95 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e274 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e369 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e35-54 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e214 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e577 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e791 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e55-64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e118 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e222 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e340 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u0026nbsp;\u003c/strong\u003e(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003ePrague\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e60 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e131 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e191 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eCzechia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e200 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e532 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e732 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eMoravia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e167 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e410 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e577 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTown population\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026lt;5000 inhabitants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e130 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e356 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e486 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e5000 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e297 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e717 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1014 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e27 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e40 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e67 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e275 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e718 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e993 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e125 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e315 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e440 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e286 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e700 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e986 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eEntrepreneur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e65 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e93 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e14 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e26 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e40 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eMaternal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e29 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e126 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e155 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eRetired (medical grounds)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e20 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e48 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e68 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e21 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e30 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e24 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e73 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e97 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e17 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e14 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e31 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e98 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e189 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e287 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003ePartnership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e100 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e299 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e399 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e130 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e387 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e517 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e88 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e186 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e274 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eWidow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e11 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChildren\u003c/strong\u003e (n, % Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e286 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e799 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e7085 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of children\u003c/strong\u003e (mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.96 (1.86-2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.86 (1.8-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.89 (1.83-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal income\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026lt;20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e123 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e285 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e408 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e20,000-30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e139 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e372 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e511 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e30,001-40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e87 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e206 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e293 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026gt;40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e44 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e91 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e135 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e34 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e119 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e153 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026lt;40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e138 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e292 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e430 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e40,000-60,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e121 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e322 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e443 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e60,001-80,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e83 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e217 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e300 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026gt;80,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e47 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e112 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e159 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e38 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e130 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e168 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior HPV infection\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e18 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e72 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e90 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e368 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e895 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1263 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e41 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e106 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e147 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPV vaccination\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes, both doses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e56 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e136 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e192 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes, incomplete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e14 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e21 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e35 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e319 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e813 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1132 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e38 (103%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e103 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e141 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCervical cancer in family\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e31 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e71 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e102 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e353 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e887 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1240 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e43 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e115 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e158 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic diseases\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e127 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e296 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e423 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e281 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e725 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1006 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e19 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e52 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e71 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior Breast Cancer Screening\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e161 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e329 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e490 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e72 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e93 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e162 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e30 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e33 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior Colorectal Cancer Screening\u003c/strong\u003e (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e67 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e127 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e194 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e107 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e162 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e269 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e29 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e34 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn order to further elucidate the factors influencing the increase in motivation to screen if self-administered, at-home HPV test would be available, additional questions from the survey pertaining to HPV self-testing were analyzed. These findings are summarized in Table 5. Women who reported increased motivation to participate screening if a self-administered HPV test were available reported significantly higher willingness to use such a test compared to those who reported no change in motivation (mean score 4.28 vs. 3.32). Similarly, the role of health insurance coverage as a motivator was higher in this group (mean score of 4.2 vs. 3.36). These findings suggest that perceived convenience and affordability are key facilitators of self-test acceptance, particularly among women already inclined to increase their screening participation. Women who reported an increased motivation to screen were significantly \u003cstrong\u003eless concerned\u003c/strong\u003e about performing the self-test incorrectly (mean score 3.25 vs. 3.64). However, when asked about their willingness to seek follow-up care with a gynecologist if the self-test returned a positive result, the pattern was reversed: women in the \u003cstrong\u003enon-motivated\u003c/strong\u003e group showed higher willingness (mean score 3.64 vs. 3.25). This could reflect a greater trust in the conventional medical pathway among those less inclined to adopt self-testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Scores of selected factors pertaining to HPV self-test used to screen for cervical cancer by increase in motivation to participate in cervical cancer screening if a self-administered HPV test is available\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncreased motivation to screen\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=427 (29%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo increase in motivation to screen\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=1073 (71%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=1500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness to use self-test\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.28 (4.21-4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.32 (3.26-3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.6 (3.54-3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncreased willingness if covered by health insurance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.2 (4.12-4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.36 (3.3-3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.61 (3.55-3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFear of test misuse\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3.25 (3.15-3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.64 (3.58-3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.53 (3.48-3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness to follow up with physician after self-test\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mean, 95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3.25 (3.15-3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.64 (3.58-3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.53 (3.47-3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6 presents the results of logistic regression analyses of the association of increase in motivation to screen if self-administered, at-home HPV test would be available and further factors pertaining to HPV self-testing as predictors. Two models were constructed, model 1 contains the four domains as covariates and model 2 further adjusts for age, occupation, marital status and having children (E.g., the four factors that were significantly different in the univariate analyses shown in table 4). In both logistic regression models, the strongest predictor of increased motivation was the \u003cem\u003ewillingness to use a self-test\u003c/em\u003e (OR=5.8, 95% CI: 4.3\u0026ndash;8.1 in the adjusted model). The perception that insurance coverage would increase willingness was also significantly associated with motivation (adjusted OR=2.0, 95% CI: 1.6\u0026ndash;2.5). On the other hand, fear of test misuse was associated with a lower likelihood of increased motivation (OR=0.65, 95% CI: 0.55\u0026ndash;0.77), similarly to \u003cem\u003ewillingness to follow up with a physician after a positive self-test\u003c/em\u003e\u0026nbsp; (OR=0.46, 95% CI: 0.36\u0026ndash;0.59), suggesting that those already inclined to follow up may not see self-testing as increasing their motivation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Logistic regression models of the association between selected factors pertaining to HPV self-test and the increase in motivation to participate in cervical cancer screening if a self-administered HPV test is available\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness to use self-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.9 (4.4-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5.8 (4.3-8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncreased willingness if covered by health insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.9 (1.5-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.0 (1.6-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFear of test misuse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.63 (0.54-0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.65 (0.55-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWillingness to follow up with physician after self-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.46 (0.35-0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.46 (0.36-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e=0.414\u003c/p\u003e\n \u003cp\u003eAUC=0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e=0.442\u003c/p\u003e\n \u003cp\u003eAUC=0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Model includes only the scores of the selected factors pertaining to HPV self-test as predictors\u003c/p\u003e\n\u003cp\u003e**Model in addition to model 1 adjusted for age, occupation, marital status and having children\u003c/p\u003e\n\u003cp\u003eTable 7 presents the reliability and internal validity of the CHBMS domains used in this study. All domains demonstrated high reliability, with Cronbach\u0026rsquo;s alpha values ranging from 0.87 (Benefits) to 0.95 (Self-Efficacy), indicating strong internal consistency. Ceiling effects were low across most domains (0.5%\u0026ndash;1.8%), except for Benefits (13.3%) and Self-Efficacy (11.8%), suggesting slightly higher frequencies of answers on the higher end of the scale in these domains. Floor effects were minimal (0.6%\u0026ndash;2.6%), except for Susceptibility (17%, 95% CI: 15%\u0026ndash;19%), indicating limited clustering of answers a at the lower end of the scale. In summary, these parameters confirm the CHBM tool\u0026rsquo;s robust psychometric properties for assessing cervical cancer screening beliefs in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Reliability and validity of the CHBM domains used in the survey: Cronbach\u0026rsquo;s Alpha with 95% CI, Ceiling Effect and Floor Effect with 95%CI\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHBM Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s Alpha (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCeiling Effect (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFloor Effect (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSusceptibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.93 (0.93-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.6% (0.3%-1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e17% (15%-19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeverity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.88 (0.87-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.8% (1.2%-2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.6% (1.9%-3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.87 (0.85-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e13.3% (11.6%-25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.6% (0.3%-1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarriers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.93 (0.92-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.5% (0.3%-1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.5% (1.8%-3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Efficacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.95 (0.95-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e11.8% (10.2%-13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.9% (0.5%-1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a nationally representative survey using the CHBMS to identify predictors of cervical cancer screening participation among eligible women in Czechia. In addition, we explored the potential of self-administered HPV testing to enhance screening motivation and participation. We found that 61% of eligible women reported prior participation in cervical cancer screening. Their participation was strongly predicted by perceived benefits (OR 1.6, 95%CI:1.3-2), self-efficacy (OR 1.6, 95%CI: 1.2-2) and perceived barriers (OR 0.4, 95%CI:0.32-0.51) even after adjusting for age, education and income. The availability of a home-based self-administered HPV test instead of a cytological exam would increase the motivation to participate in the screening program in 29% of the eligible women. This motivation is strongly predicted by the willingness of women to the self-test (OR 5.8, 95%CI:4.3-8.1) and covering the cost by health insurance (OR 2. 95%CI: 1.6-2.5), while fear of test misuse and the willingness to follow-up with the physician after the self-test reduced significantly this motivation \u0026ndash; OR 0.65 (95%CI: 0.55-0.77) and 0.46 (95%CI: 0.36-0.59), respectively.\u003c/p\u003e\n\u003cp\u003eThe proportion of women who previously participated in cervical screening found in our study was 61%, which is in line with the participation reported by national authorities (56,2 % screened among the 25\u0026ndash;59 year old women in 2021) [21-23], but still lags behind the 70% threshold outlined by the WHO [3] and suggested by evidence to be a critical participation rate for an effective cervical cancer screening program [7, 14, 24, 25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of demographic characteristics, those that participated previously in cervical cancer screening tended to be older\u0026nbsp;(mean 45.4 vs. 41.1 years in non-participants), had higher education (33% university-educated vs. 25%), were married or in a partnership (65% vs. 55%), had children more often (78% vs. 22%) and higher household income. Previous studies similarly showed higher participation among women with higher attained education [30, 32] - especially in early stages of population-based screening programs [36], among those with higher income [34], higher age [25, 35], and among un-married women [31, 33]. Thus, demographically, our findings are well aligned with the current literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that the participation was driven mainly by the benefits, barriers, and self-efficacy domains of the CHBM. These findings suggest that women who see strong, concrete positive outcomes from cervical cancer screening, and those who believe that they are able to arrange and complete their screening successfully are significantly more likely to participate. These findings are confirmed by a large-scale systematic review that recognized the awareness of screening benefits and the confidence (self-efficacy) of women in their ability to successfully go through the screening procedures as principal conducive factors for participation [37]. These domains strongly predicted cervical cancer screening participation also in other studies that applied the HBM model [38-40]. The susceptibility domain (E.g., the perception of risk of getting cervical cancer) had a relatively less strong, but significant association with cancer participation (OR 1.3, 95%CI:1.1-1.5) \u0026ndash; the pattern confirming similar findings of published studies [32, 37, 41]. On the other hand, higher scores of perceived barriers were strong predictors or non-participation in screening in our study, suggesting that practical, emotional, cultural/personal or health system/structural obstacles are a major hindering factor for participation. This is widely confirmed by previous studies in various populations [32, 37-41]. In contrast, perceived severity was not significantly associated with cervical cancer screening participation, which may reflect lower awareness of the seriousness of the disease in the population \u0026ndash; further studies are needed to elucidate this finding. While the substantial hindering effect of perceived barriers can be seen as a problem, it also may be an opportunity to increase the participation by redesigning the screening in a way that overcomes some of these barriers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIntroduction of HPV-test based screening may be an important milestone and Czechia is in the process of transitioning towards this method of screening [21, 35]. Our study found that nearly a third (29%) of eligible women would be more likely to participate in the screening if a home-based HPV-test would be available (as opposed to a cytological exam). Compared to those that would not be more motivated, they were older (mean age 45.1 vs. 42.9 years), more likely to be single or divorced (44% vs. 35%), less likely to have children (67% vs. 75%), and also less likely to participate in breast or colorectal cancer screening. These results suggest that self-administered HPV testing could address key barriers to screening, particularly for women who find clinic-based Pap smears inconvenient or uncomfortable. Findings from published studies report that HPV self-sampling increased the number of non-attenders screened [42], and suggest that it is a far superior way to reach under-screened populations compared to mail-in invitations [43, 44]. Furthermore, this method has been successfully piloted in Czechia [45] and Slovakia (a country with similar social and cultural background) [46], which supports its feasibility on scale. However, there are issues that needs to be addressed: while we found that among eligible women in Czechia willingness of the use of an HPV self-test and coverage of cost by health insurance are strongly associated with their increased motivation to participate, willingness to follow up with physicians if needed may hinder their participation. Thus, without robust follow-up systems, the benefits of increased screening participation could be undermined by missed diagnoses or delayed treatment. Czechia\u0026rsquo;s ongoing transition to HPV-based screening at ages 35 and 45 provides an opportunity to integrate self-sampling with clear follow-up pathways, such as automated reminders or telehealth consultations, to address this challenge.\u003c/p\u003e\n\u003cp\u003eSome limitations should be considered when interpreting these findings. First, the study relies entirely on self-reported data and this may be subject to recall bias. Second, the use of online and telephone surveys may have underrepresented women with limited digital access, potentially introducing selection bias. Therefore, when interpreting and generalizing the findings of this study, these factors should be observed. However, the fact the participation rate found in our study is largely in line with official data released by national authorities in Czechia suggests that the sampling of the study was robust and produced a representative and valid dataset. Furthermore, a validated and established tool has been used to collect data on health beliefs which further supports the validity of our findings.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the critical role of perceived barriers, benefits, and self-efficacy in driving cervical cancer screening participation in Czechia. The potential of self-administered HPV testing to increase motivation among underserved groups offers a promising strategy to enhance screening coverage. By addressing barriers, promoting benefits, and ensuring robust follow-up systems, Czechia can make significant progress toward meeting the WHO\u0026rsquo;s 70% coverage target and reducing its cervical cancer burden. These findings provide a foundation for targeted public health interventions and future research to advance cervical cancer prevention in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003e Our research was conducted in accordance with the Declaration of Helsinki, the ethical approval for the study was granted by the Ethics Committee of the Faculty of Health Sciences and Social Work at Trnava University in Trnava, reference number EK-2/2K/2025.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003e Informed consent was obtained electronically from all participants prior to initiating the online questionnaire. At the outset of the survey, each participant was presented with a detailed information sheet and a consent form outlining the purpose of the study, data handling procedures, and their rights. Only those who actively provided consent were granted access to the questionnaire; without this confirmation, progression to the survey items was not possible.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003e This research was funded by Slovakia's plan for resilience and recovery, grant 09I01-03-V04-00058 and by the Institutional Support for the Long‑term Conceptual Development of a Research Organization, provided by the Ministry of Education, Youth and Sports of the Czech Republic.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMM, KJ, JF conceived the study and designed the research. Data collection was undertaken by KJ, JF, DM, DP, JM and PS. MM, DP, DM, and KJ performed the data analysis with insights from JM, KP, and JF. KJ and MM drafted the initial manuscript, while all other authors critically revised it for intellectual content. All authors reviewed and approved the final manuscript and agree to be accountable for all aspects of the work in accordance with ICMJE criteria.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available on reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Cancer Observatory. Cancer Today [https:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e//gco.iarc.who.int/media/globocan/factsheets/cancers/23-cervix-uteri-fact-sheet.pdf]\u003c/span\u003e\u003cspan address=\"http:////gco.iarc.who.int/media/globocan/factsheets/cancers/23-cervix-uteri-fact-sheet.pdf]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal Cancer Observatory. 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Investigating feasibility of 2021 WHO protocol for cervical cancer screening in underscreened populations: PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC). BMC Public Health. 2022;22(1):1356.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer, Screening, Participation, Human Papilloma Virus, Self-test, Champions Health Belief Model Scale","lastPublishedDoi":"10.21203/rs.3.rs-7719792/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7719792/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThe aim of this study was to use the Champions Health Belief Model Scale (CHBM) to measure beliefs and attitudes influencing participation of eligible women in Czechia in the national cervical cancer screening program and to analyze the potential increase in motivation to participate in screening if a home-based HPV-self test would be available.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional population-based study was conducted on a representative sample of women eligible for cervical cancer screening in the Czech Republic in March-April 2025. Overall, 1500 women were recruited for the survey and interviewed using the CHBM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e We found that 61% of eligible women reported prior participation in cervical cancer screening. Their participation was strongly predicted by perceived benefits (OR 1.6, 95%CI:1.3-2), self-efficacy (OR 1.6, 95%CI: 1.2-2) and perceived barriers (OR 0.4, 95%CI:0.32\u0026ndash;0.51) even after adjusting for age, education and income. The availability of a home-based self-administered HPV test instead of a cytological exam would increase the motivation to participate in the screening program in 29% of the eligible women. This motivation is strongly predicted by the willingness of women to the self-test (OR 5.8, 95%CI:4.3\u0026ndash;8.1) and covering the cost by health insurance (OR 2. 95%CI: 1.6\u0026ndash;2.5), while fear of test misuse and the willingness to follow-up with the physician after the self-test reduced significantly this motivation \u0026ndash; OR 0.65 (95%CI: 0.55\u0026ndash;0.77) and 0.46 (95%CI: 0.36\u0026ndash;0.59), respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study highlights the critical role of perceived barriers, benefits, and self-efficacy in driving cervical cancer screening participation in Czechia. The potential of self-administered HPV testing to increase motivation among underserved groups offers a promising strategy to enhance screening coverage. By addressing barriers, promoting benefits, and ensuring robust follow-up systems, Czechia can make significant progress toward meeting the WHO\u0026rsquo;s 70% coverage target and reducing its cervical cancer burden. These findings provide a foundation for targeted public health interventions and future research to advance cervical cancer prevention in the region.\u003c/p\u003e","manuscriptTitle":"The Role of Health Beliefs and HPV Self-Testing in Cervical Cancer Screening Participation in Czechia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 07:37:23","doi":"10.21203/rs.3.rs-7719792/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-18T23:56:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T20:30:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T07:37:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T18:09:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93495432666014692527794330356176106515","date":"2025-12-03T00:36:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64748600600597335095048595787644167973","date":"2025-12-01T13:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261569490952979444809040015851806526085","date":"2025-12-01T03:53:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62657425314420962057460229571588101890","date":"2025-11-28T14:00:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327506444155170995732518632951902017311","date":"2025-11-19T15:01:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334197550525611552865074874226454919910","date":"2025-10-29T07:58:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291682641096611978887338148770209533274","date":"2025-10-22T04:51:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T02:06:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-02T09:31:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T12:18:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T12:17:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-09-26T08:48:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04b7624c-516a-41e1-9b56-c9074f8636a7","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:03:22+00:00","versionOfRecord":{"articleIdentity":"rs-7719792","link":"https://doi.org/10.1186/s12905-026-04467-2","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2026-04-23 15:59:47","publishedOnDateReadable":"April 23rd, 2026"},"versionCreatedAt":"2025-11-03 07:37:23","video":"","vorDoi":"10.1186/s12905-026-04467-2","vorDoiUrl":"https://doi.org/10.1186/s12905-026-04467-2","workflowStages":[]},"version":"v1","identity":"rs-7719792","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7719792","identity":"rs-7719792","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}