Health Literacy and Its Determinants Among Pregnant Women in Portugal

Health Literacy and Its Determinants Among Pregnant Women in Portugal | 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 Health Literacy and Its Determinants Among Pregnant Women in Portugal Nuno Ferreira, Manuela Ferreira, Eduardo Santos, Sofia Ferreira, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6396883/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2025 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Health literacy is a key determinant of health outcomes and equity, particularly during pregnancy, a period marked by increased information needs and critical health decisions. Despite its importance, data on health literacy among pregnant women in Portugal remain scarce. This study aimed to assess general health literacy levels and their associations with sociodemographic, health-related, and pregnancy-specific factors in a large sample of pregnant women from the district of Viseu, Portugal. Methods A cross-sectional study was conducted with 886 pregnant women aged 18 years or older, using the validated HLS 19 -Q12 instrument to measure general health literacy. Data collection occurred between October 2023 and May 2024 using a multimodal approach (online, interview, and paper-based). Health literacy was categorized into four levels and also dichotomized as limited versus not limited. Descriptive statistics, bivariate analyses, and binary logistic regression were performed to identify determinants of limited health literacy. Results The mean general health literacy score was 68.3 (SD ± 10.9). A total of 46.7% of participants were classified as having limited health literacy. Higher prevalence was observed among women aged 18–29, those born outside Portugal, with lower education, in undifferentiated professions, and facing financial hardship. In the final adjusted logistic regression model, significant predictors of limited health literacy included being born outside Portugal (adjusted OR 2.43; 95% CI: 1.56–3.80), having lower education (up to high school), holding lower-skilled occupations, financial difficulties, and rating current health as equal or worse. Body Mass Index prior to pregnancy was also associated with literacy levels. The model showed good discriminatory ability (area under the ROC curve = 0.78). Conclusions A considerable proportion of pregnant women demonstrated limited health literacy, especially among socioeconomically vulnerable groups. These findings highlight the need for literacy-sensitive prenatal care practices and targeted public health interventions that address both individual and structural determinants. Incorporating health literacy assessments and tailored communication strategies in antenatal care could support informed decision-making, promote equity, improve maternal and neonatal outcomes. Health Literacy Health Inequities Pregnant Women Maternal Health Prenatal Care Social Determinants of Health Public Health Figures Figure 1 Figure 2 Figure 3 Background Health literacy, defined as the ability to access, understand, appraise, and apply health-related information, is a critical determinant of health outcomes and equity ( 1 ) ( 1 ). It is particularly crucial during pregnancy, a period marked by heightened health demands and complex decision-making, impacting both maternal and child health outcomes. Pregnant women must navigate diverse sources of health information and collaborate effectively with healthcare providers, making health literacy an indispensable competency ( 2 ). Research highlights significant associations between low health literacy and adverse pregnancy outcomes, including gestational diabetes, low birth weight, and increased maternal stress ( 3 ). Furthermore, inadequate health literacy is linked to delays in accessing prenatal care, limited adoption of health-promoting behaviors, and lower engagement with antenatal education ( 4 ). These challenges underscore the importance of addressing health literacy disparities, especially in vulnerable populations such as immigrants, socioeconomically disadvantaged groups, and women with limited educational backgrounds ( 5 ). Migrant women, in particular, may face compounded vulnerabilities related to cultural, social, and structural barriers that limit access to adequate reproductive and maternal healthcare services, as highlighted in recent umbrella reviews focused on equity in sexual and reproductive health outcomes ( 6 ). Studies have also shown that low health literacy affects healthcare utilization and adherence to medical recommendations, potentially exacerbating inequalities in maternal and neonatal health outcomes ( 7 ). The percentages of health literacy (HL) levels among pregnant women vary significantly across cultural, socioeconomic, and geographical contexts, as well as depending on the measurement tools used. International studies provide valuable insights into these variations. Globally, between 15% and 50% of pregnant women are classified as having inadequate or limited HL, with significant differences observed across countries and populations ( 3 , 5 ). Approximately 33% demonstrate sufficient HL, while only 10–20% achieve excellent HL levels ( 4 ). For example, in the United States, 38% of pregnant women were found to have low HL, which was associated with poorer comprehension of prenatal tests and medical guidelines ( 8 ). Similarly, in Turkey, 33.9% of pregnant women had sufficient HL, while 66% exhibited limited HL ( 5 ). In Europe, a study from Denmark highlighted that immigrant pregnant women have lower HL levels compared to native-born women ( 4 ). In Iran, 42.8% of pregnant women were found to have adequate HL, whereas 15.5% had inadequate HL ( 9 ). The investigations specifically focused on health literacy during pregnancy remains limited, despite the significance of this population as a key target for public health interventions. Nonetheless, some initiatives have begun to explore the topic indirectly or within localized contexts, such as studies examining health behaviors during pregnancy. However, studies in Portugal with representative data, or large samples, on health literacy levels among pregnant women, as well as associated factors like sociodemographic determinants and cultural barriers, remain unavailable. For example, in a study of 404 Portuguese pregnant women, the authors found that 50.5% had limited health literacy ( 10 ). In contrast, another study in Portugal, reported that only 25.8% of a sample of 264 pregnant women demonstrated limited health literacy ( 11 ). These variations underscore the impact of differing methodologies, contexts, and population characteristics on the outcomes of health literacy assessments. As emphasized by Zibellini et al., the design of interventions to improve health literacy must account not only for the accessibility of information but also for the cultural and socioeconomic barriers that impede effective healthcare utilization ( 7 ). Pregnancy represents a pivotal period in the life cycle, where individual, familial, and transgenerational factors converge from somatic, psychological, and cultural perspectives. It should be viewed not only as a phase leading to specific outcomes but also as a strategic window for interventions that promote lasting changes in habits and behaviors. These changes can benefit the woman, her child, and the entire family unit across the life span. Examples include adopting healthier eating habits, increasing physical activity, and cessation of smoking and psychoactive substance use. Furthermore, pregnancy offers a critical opportunity to identify and address conditions that may pose risks for future health complications, such as gestational diabetes and pre-eclampsia. Evidence now demonstrates that the presence of these conditions during pregnancy significantly elevates the long-term risk of developing chronic diseases, including diabetes and cardiovascular disorders, in women ( 12 ). The concept of pregnancy as a "teachable moment" for improving health literacy (HL) is well-supported in the literature, emphasizing that women are often highly motivated to adopt health-promoting behaviors during this time. This period is seen as an opportunity to influence behaviors such as nutrition, physical activity, and healthcare engagement, benefiting both maternal and child health ( 7 , 8 , 13 , 14 ). Additionally, the regular interaction with healthcare systems during pregnancy enhances the potential for targeted interventions aimed at fostering long-term health literacy and behavioral changes ( 2 , 3 ). This study aims to assess general health literacy among pregnant women in Portugal, focusing on its relationship with sociodemographic, socioeconomic, and healthcare-related factors. By analyzing levels of general health literacy in a large sample from the district of Viseu, this study seeks to identify determinants of limited health literacy, providing insights to support equitable access to prenatal care improving maternal and neonatal outcomes. Methods Development The World Health Organization’s report “Health Literacy: The Solid Facts” ( 15 ) emphasized the importance of regularly measuring health literacy (HL) across populations using standardized and theory-based approaches. This recommendation aligns with the HLS-EU conceptual framework, which defines HL comprehensively and provides validated measurement tools, such as the HLS-EU-Q47 and its shorter derivatives, the HLS-EU-Q16, HLS-EU-Q12, and HLS-EU-Q6 ( 16 ). Recognizing the need for high-quality, internationally comparable data on population and organizational HL, the WHO established the Action Network on Measuring Population and Organizational Health Literacy (M-POHL) in 2018 ( 17 ). Building on the foundation of the HLS-EU study, the HLS 19 survey aimed to assess general population HL comprehensively across participating countries. At a minimum, HL was measured using the HLS 19 -Q12, a validated short form. Additionally, the survey included optional modules to evaluate specific areas of HL: ( 1 ) digital HL (8 items), ( 2 ) communicative HL (long form with 11 items, short form with 6 items), ( 3 ) navigational HL (12 items), and ( 4 ) vaccination HL (4 items). These modules served to enhance the understanding of specific HL dimensions and validate the discriminant validity of the HLS 19 -Q12 ( 18 ). The Portuguese version of the HLS 19 -Q12 has been validated in a representative sample of individuals aged 16 and older, including 643 women (52%) ( 19 ), on the other hand, the HLS-EU study identified, on average, a weak but statistically significant β coefficient indicating that females tend to have higher health literacy scores than males. However, this relationship was not statistically significant in certain countries included in the HLS-EU analysis ( 18 ). The scale evaluates three key domains of health literacy: health care, disease prevention, and health promotion, across four dimensions of health information processing: accessing, understanding, appraising, and applying health-related information. Participants rated the 12 items on a 4-point Likert scale, ranging from "1 = very difficult" to "4 = very easy." The general health literacy score was calculated by summing the responses to all valid items and rescaling the total to a 0-100 scale, with higher scores indicating better health literacy ( 16 ). Similarly, seven sub-indices corresponding to the three domains and four dimensions were calculated using the same summing and rescaling method. If fewer than 80% of the items required for a given calculation were valid, the respective score was deemed "missing." The overall health literacy score and the seven sub-indices were categorized into four levels: "inadequate" (0–50, inclusive), "problematic" (50–66.67, inclusive), "sufficient" (66.67–83.33, inclusive), and "excellent" (above 83.33). Additionally, a composite variable combining the "problematic" and "inadequate" levels was created to represent "limited health literacy." While the original developers of the HLS 19 -Q12 proposed two distinct methods for categorizing health literacy scores, the approach applied in this study was selected due to its broader acceptance ( 16 ). Authorization to use the HLS 19 -Q12 instrument in this research was obtained from the HLS 19 Consortium at: link: https://m-pohl.net/Design_Methods . Testing All participants provided written informed consent before any procedures were conducted, following prior ethical approval by the Institutional Health Ethics Committees of the Tondela Viseu Hospital Center (reference 13/29/09/2023) and the Central Regional Health Administration (process number 124–2023). Additionally, all procedures adhered to the ethical principles outlined in the Declaration of Helsinki ( 20 ). To facilitate participation and ensure convenience for pregnant women, the survey, which included the HLS 19 -Q12 instrument, was offered in three formats to achieve a homogeneous sample. Participants could respond digitally via a QR code or link distributed during pregnancy appointments (28.8%), through an interview conducted in primary healthcare or hospital settings (45.4%), or by self-completing a paper-based version (25.8%). The latter option had a response rate of 76%. The inclusion criteria required participants to be at least 18 years old, at least 10 weeks pregnant, and able to read and comprehend Portuguese. No exclusion criteria were established. Data collection was carried out between the 15th of October 2023 and the 15th of May 2024. Based on gestational ages at enrolment, the estimated delivery window for the surveyed sample spans and considering the average number of births recorded annually in the district of Viseu during this period, approximately 1.800 births per year, the 886 pregnant women who participated in the study represent approximately 49% of the expected pregnancies in the region. This level of participation supports the robustness and regional representativeness of the sample, strengthening the external validity and applicability of the findings to the local pregnant population. Sociodemographic characteristics, lifestyle-related health behaviors, health-related variables, and gynecological and obstetric histories of the pregnant women participants were collected at the outset of the survey. Collected sociodemographic variables included age, which was categorized into age groups (18–29 years; 30–39 years; ≥ 40 years), nationality (Portugal; another country), formal education (up to 2nd cycle of primary education; 3rd cycle of primary education; high school; university education); marital status (single, married/common-law marriage; divorced/separated/widow; employment status (working professionally; not working professionally); profession/ occupation (intellectual and scientific; techniques; undifferentiated); residence typology (own house/apartment; rented house/apartment; social house/apartment); situation in the work (worker on their own; family worker; worker on account of other; domestic, student, unemployed or retired; residence typology (own house/apartment; rented house/apartment; home/apartment of relatives; social house/institution); training in a healthcare profession (yes; no). We collected data on health behaviors and lifestyles, including smoking status (never smoked; smoked before pregnancy; quit during pregnancy; or occasional smoking), exposure to tobacco smoke (yes or no), and consumption of alcohol or psychoactive substances (never; used before pregnancy; quit during pregnancy; or occasional use). Information on physical activity levels was also gathered, categorized as never, never due to medical restrictions, occasional, light, or heavy. Dietary habits were assessed through fruit and vegetable consumption (never; occasional use that correspond to less than one day per week and 1 day; 2 days and 3 days grouped as light use; 4 days, 5 days, 6 days, 7 days grouped as heavy). Additionally, participants reported their pre-pregnancy Body Mass Index (BMI), classified as underweight, normal weight, overweight, or obese. Health status variables were also included, such as prior self-perceived health status (categorized as bad or very bad; fair; good or very good) and current self-perceived health status (much worse or worse; equal; better or much better). Self-reported chronic diseases or disabilities were recorded (yes or no), along with the perceived ease of managing these conditions (categorized as easy or very easy; hard or very difficult). Finally, participants were asked whether health problems limited their daily activities (not limited; limited; greatly limited). Data on gynecological variables included the use of contraceptive methods (yes or no) and cervical cytology status, categorized as never performed, within the last 12 months, more than 1 year but less than 2 years ago, more than 2 years but less than 3 years ago, or not performed in the last 3 years. For obstetric history, variables collected encompassed current gestational age groups (10–13 weeks, 14–27 weeks, or 28–41 weeks), attendance at preconception care consultations (yes or no), whether the pregnancy was planned (yes or no), the number of weeks pregnant at the time the pregnancy was discovered, and whether the pregnancy was classified as high-risk (yes or no). Additional information included the number of children, the location of antenatal care (health center, private clinic, or hospital), intention to breastfeed (yes, not yet decided, no, or cannot breastfeed), and intention to attend a childbirth preparation program (no, already attending, plans to attend, or undecided/cannot attend). Statistical analysis Data analysis was carried out using IBM SPSS Statistics®, version 29.0 (IBM Corp, Armonk, NY, USA), with a 5% significance level. Descriptive statistics were used to summarize the sample's sociodemographic profile, lifestyle-related health behaviors, health-related characteristics, and gynecological and obstetric histories. The evaluation also included the computation of mean scores and standard deviations for overall health literacy and its sub-dimensions. Furthermore, the distribution of participants across the four health literacy categories and dichotomized variables was reported as percentages, based on valid responses. The number of respondents included in the scoring and the extent of missing data were also recorded. Bivariate analyses were conducted using Fisher’s exact test and chi-square tests to identify associations between health literacy levels and various factors. General health literacy, dichotomized into “limited” and “not limited,” served as the dependent variable in a binary logistic regression analysis. Independent variables with a p-value < 0.10 in bivariate analyses were included as predictors of limited health literacy in the logistic regression. The first model applied a forward likelihood ratio (LR) approach, while a second model used an enter method to add variables of interest not selected in the initial analysis. Any variables related to age, gender, education, residence, or financial household status excluded from the first model were reintroduced in a subsequent block. The findings are presented as crude odds ratios (cOR) and adjusted odds ratios (aOR), along with 95% confidence intervals (95% CI). The residual probabilities of the final adjusted logistic regression model were utilized to calculate the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve. Results Sample and items description The study included a sample of 886 pregnant women with a mean age of 31.09 years (± 5.60 years). The participants ranged in age from 18 to 51 years. More than half of the sample (53.3%) were aged between 30 and 39 years, 39.7% were between 18 and 29 years, and 7.0% were 40 years or older. Most participants were Portuguese nationals (81.2%), with 18.8% born in other countries. Educational attainment was relatively high, as 42.6% had completed university education, and 40.7% had finished high school. Regarding marital status, 73.4% were married or in a common-law relationship, while 25.2% were single. In terms of employment, 77.8% of the participants were professionally active. Among these, 52.5% were employed in technical occupations, 27.3% held intellectual or scientific roles, and 20.2% worked in undifferentiated professions. Additionally, 69.2% worked for others, 8.1% were self-employed, and 1.2% were family workers. However, 21.4% of participants were not engaged in professional work, including students, domestic, unemployed individuals, or retirees. Regarding housing, 46.4% owned their homes, 32.5% rented, and 20.8% lived with relatives, while 0.3% resided in social housing. Notably, 15.2% of participants reported having training in a healthcare profession, while 84.8% did not. The analysis of financial stress among participants reveals that a majority faced challenges in meeting their expenses. Specifically, 61.7% of participants reported finding it "difficult" or "very difficult" to pay their expenses, indicating a significant level of financial strain. Conversely, 38.3% of participants found paying expenses to be "very easy" or "easy," suggesting a minority experienced financial ease. Further details can be found in Table 1 . Table 1 Sociodemographic characteristics of the sample (n = 886) Sociodemographic characteristics n = 886 Age (mean ± SD) 31.09 ± 5.60 Age Groups (years) n (%) 18–29 352 (39.7%) 30–39 472 (53.3%) ≥ 40 62 (7.0%) Nationality Portugal 719 (81.2%) Another country 167 (18.8%) Formal education 2nd cycle of primary education 33 (3.7%) 3rd cycle of primary education 115 (13%) High school 361 (40.7%) University education 377 (42.6%) Marital status Single 223 (25.2%) Married/common-law marriage 650 (73.4%) Divorced/separated/widow 13 (1.5%) Employment status Working professionally 689 (77.8%) Not working professionally 197 (22.2%) Profession/ occupation Intellectual and scientific 242 (27.3%) Techniques 465 (52.5%) Undifferentiated 179 (20.2%) Residence typology Own house/apartment 411 (46.4%) Rented house/apartment 288 (32.5%) Relatives house/apartment 184 (20.8%) Social house/apartment 3 (0.3%) Situation in the work Worker on their own 72 (8.1%) Family worker 11 (1.2%) Worker on account of other 613 (69.2%) Domestic_Student_Unemploymen_retired 190 (21.4%) Training in a healthcare profession No 751 (84.8%) Yes 135 (15.2%) Payment of expenses Very Easy_easy 339 (38.3%) Difficult_ Very difficult 547 (61.7%) 1 SD, Standard deviation. Health behaviors and lifestyles and health status were examined to better understand factors influencing maternal health. Regarding smoking behavior, 74.0% of participants reported never smoking, while 13.9% smoked before pregnancy, 4.9% quit during pregnancy, and 7.2% smoked occasionally. Tobacco smoke exposure was reported by 32.3% of participants. Alcohol or psychoactive substance use were reported by 33.2% of pregnant women. Among these, 16.8% consumed substances before pregnancy, 7.6% quit during pregnancy, and 8.8% used them occasionally. Physical activity levels varied, with 34.9% engaging in light activity, 30.2% in heavy activity, and 25.4% reporting no physical activity. Fruit and vegetable consumption were notably high, with 89.4% reporting heavy intake. Pre-pregnancy BMI classifications revealed that 55.9% had a normal BMI, 24.5% were overweight, and 15.0% were classified as obese. Regarding self-perceived health, 69.8% rated their previous health as good or very good, while 29.0% considered it fair, and 1.2% described it as bad or very bad. During pregnancy, 11.2% felt their health had worsened, while 81.7% reported no change, and 7.1% indicated improvement. Chronic diseases or disabilities were reported by 19.2% of participants, with 30.6% finding it difficult to manage these conditions. Additionally, 12.8% experienced no activity limitations, 5.5% reported some limitations, and 0.9% faced significant limitations. Detailed results are provided in Table 2 . Table 2 Health behaviors and lifestyles and health status variables of the sample (n = 886) Health behaviors and lifestyles n (%) Smoking behavior Never 656 (74.0%) Before pregnancy 123 (13.9%) Stopped in pregnancy 43 (4.9%) Occasionally 64 (7.2%) Tobacco smoke exposure Yes 286 (32.3%) No 600 (67.7%) Alcohol or psychoative subs. (PS) consumption Never 592 (66.8%) Before pregnancy 149 (16.8%) Stopped in pregnancy 67 (7.6%) Occasionally 78 (8.8%) Physical activity Never 225 (25.4%) Never: medical restriction 27 (3.0%) Occasional 57 (6.4%) Light 309 (34.9%) Heavy 268 (30.2%) Fruit and vegetable consumption Never 3 (0.3%) Occasional 5 (0.6%) Light 86 (9.7%) Heavy 792 (89.4%) BMI prior to pregnancy Underweight 41 (4.6%) Normal weight 495 (55.9%) Overweight 217 (24.5%) Obesity 133 (15.0%) Health status n (%) Previous Self-perceived health status Bad or very bad 11 (1.2%) Fair 257 (29.0%) Good or very good 618 (69.8%) Current Self-perceived health status Much worse or worse 99 (11.2%) Equal 724 (81.7%) Better or much better 63 (7.1%) Previous self-reported chronic (SRC) disease/disability Yes 170 (19.2%) No 716 (80.8%) Dealing with SRC disease/disability (n = 170) Easy or too easy 118 (69.4%) Hard or very difficult 52 (30.6%) Health problems limited the activity (n = 170) Not limited 113 (12.8%) Limited 49 (5.5%) Greatly limited 8 (0.9%) 1 SD, Standard deviation. The study also examined gynecological and obstetric variables to provide insights into participants' health profiles and pregnancy care (Table 3 ). Regarding the use of contraceptive methods, 89.7% of participants reported using contraception, while 10.3% did not. Cervical cytology results showed that 12.8% had never undergone the procedure. Among those who had, 36.6% underwent it within the last 12 months, 24.4% between one and two years ago, 13.3% between two and three years ago, and 13.0% more than three years ago. The mean gestational age was 29.60 weeks (± 10.61). Most participants were in their third trimester (28–41 weeks; 68.3%), followed by the first trimester (10–13 weeks; 19.1%) and the second trimester (14–27 weeks; 12.6%). Preconception care was reported by 57.3% of participants, while 42.7% did not attend such consultations. Planned pregnancies accounted for 66.6% of cases, while 33.4% of pregnancies were unplanned. In terms of pregnancy surveillance, the majority (88.8%) initiated care between 1 and 11 weeks of gestation, 10.7% began care between 12 and 27 weeks, and 0.5% started care at 28 weeks or later. Pregnancy risk were identified in 25.8% of participants, while 74.2% report a low risk pregnancy. Regarding parity, 57.4% of participants were experiencing their first pregnancy, while 42.6% had one or more children, with a mean of 0.57 children (± 4.21). Antenatal care sites varied, with 43.3% receiving care at both health centers and private clinics, 32.5% exclusively at health centers, 14.1% at health centers and hospitals, and 10.0% across health centers, hospitals, and private clinics. When asked about breastfeeding intentions, 92.6% of participants intended to breastfeed, 5.8% were undecided, 1.2% did not intend to breastfeed, and 0.5% reported being unable to breastfeed. Regarding childbirth preparation programs, 39.4% of participants did not attend, 32.1% were already attending, 17.6% intended to attend, and 10.9% had not yet decided or could not attend, probably because they are at risk of premature birth. Table 3 Gynecologic and obstetric history variables of the sample (n = 886) Gynecologic history Contraceptive method n (%) Yes 795 (89.7%) No 91 (10.3%) Cervix vaginal cytology Never 113 (12.8%) In the last 12 months 324 (36.6%) > 1 year ago, 2 years ago, < 3 years 118 (13.3%) Not in the past 3 years 115 (13.0%) Obstetric history Current gestational age (mean ± SD 1 ) 29.60 ± 10.61) Current gestational age groups n (%) 1st Trimester (10–13 weeks) 169 (19.1%) 2st Trimester (14–27 weeks) 112 (12.6%) 3st Trimester (28–41 weeks) 605 (68.3%) Preconception care (n = 590) Yes 338 (57.3%) No 252 (42.7%) Planned pregnancy Yes 590 (66.6%) No 296 (33.4%) Pregnancy surveillance 1–11 weeks 787 (88.8%) 12–27 weeks 95 (10.7%) ≥ 28 weeks 4 (0.5%) Pregnancy risk No 657 (74.2%) Yes 229 (25.8%) Children(s) (mean ± SD 1 ) 0.57 ± 4.21) Children(s) n (%) No 509 (57.4%) Yes 377 (42.6%) Site of the queries Health center / Private clinic 384 (43.3%) Health center 288 (32.5%) Health center / Hospital 125 (14.1%) Health center / Hospital / Private clinic 89 (10.0%) Breastfeeding Yes 820 (92.6%) Has not yet decided 51 (5.8%) No 11 (1.2%) Cannot breastfeed 4 (0.5%) Childbirth preparation program No 349 (39.4%) Already attends 284 (32.1%) Intends to attend 156 (17.6%) Has not yet decided/ Cannot attend 97 (10.9%) 1 SD, Standard deviation. Distribution of Limited Health Literacy Across Sample Subgroups The distribution of limited health literacy across sample subgroups were calculated using data from participants, ranging from 856 for the "Access" dimension of health information processing to 885 for "Health Promotion," a domain of health literacy. Specifically, the general health literacy score was based on 875 participants, while the domain scores included 875 for "Healthcare" and 879 for "Disease Prevention." For the dimensions of health information processing, the number of participants was 880 for "Understand," 883 for "Appraise," and 885 for "Apply." The variation in sample size reflects the number of pregnant women who provided at least 80% of valid responses required for the calculation of each specific score. A mean general health literacy score of 68.31 (± 10.92) was observed, which was lower than the mean scores for each of the six sub-indexes. These ranged from 68.32 (± 16.77) in the “Access” dimension of health information processing to 72.50 (± 11.30) in the “Apply” domain of health information processing. An exception was noted in the “Disease Prevention” domain, which exhibited a slightly lower mean score of 67.48 (± 12.83). The distribution of general health literacy (HL) among pregnant women, as presented in Fig. 1 , shows a substantial proportion of participants with intermediate levels of HL. Specifically, 4.2% were classified as having inadequate HL and 42.5% as problematic, indicating that nearly half of the sample experienced difficulties in accessing, understanding, or using health information. An equal proportion of participants (42.5%) demonstrated sufficient HL, suggesting functional skills to manage health-related tasks and decision-making during pregnancy. Only 10.7% of participants were classified in the excellent category, reflecting a relatively small subgroup with the highest levels of autonomy and confidence in dealing with health information. The analysis of health literacy across the three health information domains (Healthcare, Disease Prevention, and Health Promotion) reveals domain-specific patterns in the distribution of HL levels among pregnant women. In the Healthcare domain, 6.2% of participants were classified as having inadequate HL and 24.8% as problematic, while 55.1% reported sufficient HL and 13.9% excellent. These findings suggest that most women felt confident navigating the healthcare system and interacting with medical professionals, possibly due to their regular contact with antenatal care services. In the Disease Prevention domain, HL levels shifted slightly, with 7.1% of participants classified as inadequate and 33.1% as problematic. Meanwhile, 47.1% reported sufficient HL and 12.7% excellent. This domain presented the highest proportion of participants in the problematic category, indicating potential difficulties in understanding or applying preventive information, such as vaccinations, screenings, or behavioral risk avoidance strategies. The most favorable distribution was observed in the Health Promotion domain, where only 3.2% of participants had inadequate HL and 15.7% problematic, while 66.1% were classified as sufficient and 15.0% as excellent. These results suggest that participants were more confident in engaging with information aimed at maintaining or improving general well-being, such as nutrition, physical activity, or stress management during pregnancy. Overall, the data indicate that while healthcare navigation appears to be relatively well-managed, there are important gaps in understanding and acting upon preventive strategies. Strengthening communication related to disease prevention may help reduce risk behaviors and improve maternal and fetal health outcomes (Fig. 2). Health literacy performance in each of the four dimensions of information processing (access, understand, appraise, and apply) demonstrates distinct patterns. The access dimension presented the highest proportion of participants with inadequate HL (13.0%) and also the highest problematic HL level (25.5%), indicating that locating and obtaining health-related information may be the most challenging task for many pregnant women. In contrast, the apply dimension showed the lowest percentage of inadequate HL (4.6%) and problematic HL (9.9%), while presenting the highest proportion of sufficient HL (72.8%) and a relatively elevated excellent level (12.7%). This suggests that once information is understood, most participants felt capable of integrating it into their health decisions. For the understand dimension, 11.1% of participants were classified as inadequate, 15.8% as problematic, 63.3% as sufficient, and 9.3% as excellent. Regarding the appraise dimension, 8.2% were inadequate, 16.9% problematic, 63.6% sufficient, and 11.3% excellent. These results suggest that while comprehension and judgement of health information are generally adequate, they remain more complex than direct application. Overall, the data indicate that challenges are most prominent in the early stages of processing health information, particularly in access and appraisal, whereas applying information is reported more confidently. This may reflect the structured support provided in antenatal care but also highlights potential weaknesses in information navigation and evaluation skills during pregnancy (Fig. 3). Prevalence of Limited Health Literacy Across Participant Subgroups Table 4 displays the findings from combining the “Inadequate” and “Problematic” categories of general health literacy into a new construct termed “Limited Health Literacy.” Among pregnant women with a minimum of 80% valid responses to the 12 items of the HLS 19 -Q12 instrument (n = 875), 46.7% were identified as having limited health literacy. Table 4 General health literacy score means and limited health literacy by sociodemographic, health behaviors and lifestyles, health status, gynecologic and obstetric history characteristics. General HL Mean (± SD) Limited HL n (%) p- Value 1 Total (n = 875) 68.31 (± 10.92) 409 (46.7%) Sociodemographic characteristics Age Groups (years) 18–29 66.36 (± 10.42) 196 (56,2%) < 0.001 30–39 69.50 (± 11.02) 189 (40,7%) ≥ 40 70.37 (± 11.24) 24 (38,7%) Nationality Portugal 69.31 (± 11.27) 285 (40.2%) < 0.001 Another country 64.03 (± 7.94) 124 (74.7%) Formal education 2nd cycle of primary education 58.21 (± 7.22) 30 (90.9%) < 0.001 3rd cycle of primary education 63.98 (± 9.17) 80 69.6%) High school 66.47 (± 9.06) 189 (53,1%) University education 72.31 (± 11.74) 110 (29,6%) Marital status Single 67.05 (± 9.81) 113 (51.4%) < 0.001 Married/common-law marriage 68.90 (± 11.25) 284 (44.2%) Divorced/separated/widow 60.12 (± 5.87) 12 (92.3%) Profession/ occupation Intellectual and scientific 73.67 (± 11.71) 62 (25.8%) < 0.001 Techniques 68.03 (± 10.26) 204 (44.5%) Undifferentiated 61.77 (± 7.00) 143 (80.8%) Employment status Working professionally 69.87 (± 11.00) 262 (38,4%) < 0.001 Not working professionaly 62.78 (± 8.58) 147 (76,2%) Situation in the work Worker on their own 69.60 (± 11.39) 28 (40.6%) < 0.001 Family worker 66.05 (± 8.27) 5 (50.0%) Worker on account of other 69.98 (± 10.92) 233 (38.3%) Domestic_Student_Unemploymen_retired 62.54 (± 8.69) 143 (76.1%) Residence typology Own house/ apartment 70.64 (± 11.43) 141 (35,2%) < 0.001 Rented house/apartment 67.01 (± 9.82) 160 (55,7%) Home/ apartment of relatives 65.41 (± 10.21) 106 (57,6%) Social house/Institution 57.89 (± 16.43 2 (66.7%) Training in a healthcare profession Yes 76.49 (± 12.75) 29 (21,6%) < 0.001 No 66.83 (± 9.85) 380 (51,3%) Payment of expenses Very Easy_easy 73.30 (± 10.98) 76 (22.9%) < 0.001 Difficult_ Very difficult 65.26 (± 9.68) 333 (61.3%) Table 4 Cont. General HL Mean (± SD) Limited HL n (%) p- Value 1 Health behaviors and lifestyles Smoking behavior Never 68.47 (± 10.94) 284 (44.0%) < 0.001 Before pregnancy 69.67 (± 11.50) 53 (43.4%) Stopped in pregnancy 66.58 (± 10.95) 26 (60.5%) Occasionally 72.22 (± 7.69) 46 (71.9%) Tobacco smoke exposure Yes 66.23 (± 10.17) 166 (58.5%) < 0.001 No 69.31 (± 11.12) 243 (41.1%) Alcohol or PS consumption Never 68.20 (± 10.35) 260 (44.4%) 0.016 Before pregnancy 68.90 (± 11.93) 74 (50.3%) Stopped in pregnancy 71.49 (± 12.42) 27 (41.5%) Occasionally 65.31 (± 11.05) 48 (62.3%) Physical activity Never 64.89 (± 9.78) 141 (63.2%) < 0.001 Never: medical restriction 71.83 (± 7.36) 6 (22.0%) Occasional 67.93 (± 14.53) 23 (42.6%) Light 69.25 (± 10.97) 134 (43.8%) Heavy 69.82 (± 10.61) 105 (39.6) Fruit and vegetable consumption Never 71.92 (± 16.07) 1 (33.3%) < 0.001 Occasional 57.89 (± 9.60) 3 (75.0%) Light 62.81 (± 9.92) 66 (77.6%) Heavy 68.94 (± 10.82) 339 (43.3%) BMI (Body Mass Index) prior to pregnancy Underweight 67.10 (± 9.01) 17 (42.5%) 0.005 Normal weight 69.44 (± 11.54) 212 (43.4%) Overweight 67.58 (± 10.00) 100 (46.7%) Obesity 65.66 (± 9.91) 80 (60.6) Health status Previous Self-perceived health status Bad or very bad 61.00 (± 37.23) 9 (81.8%) < 0.001 Fair 64.87 (± 9.27) 162 (63.8%) Good or very good 69.87 (± 11.22) 238 (39.0%) Current Self-perceived health status Much worse ou worse 66.82 (± 11.18) 58 (58.6%) 0.003 Equal 68.08 (± 10.41) 332 (46.4%) Better or much better 73.42 (± 14.49) 19 (31.1%) Previous self-reported chronic disease or disability Yes 68.46 (± 10.25) 80 (47.3%) 0.864 No 68.27 (± 11.07) 329 (46.6%) Dealing with self-reported chronic disease (n = 170) Easy or too easy 69.25 (± 10.35) 53 (45.3%) 0.426 Hard or very difficult 66.70 (± 9.91) 27 (51.9%) Health problems have limited the activity (n = 170) Not limited 69.04 (± 10.38) 52 (46.0%) 0.299 Limited 67.81 (± 10.24) 22 (45.8%) Greatly limited 64.14 (± 8.19) 6 (75%) Table 4 . Cont. General HL Mean (± SD) Limited HL n (%) p- Value 1 Gynecologic History Contraceptive method Yes 68.67 (± 11.13) 350 (44.6%) < 0.001 No 65.11 (± 8.20) 59 (65.6%) Cervic vaginal cytology Never 62.38 (± 8.40) 82 (73.2%) 1 year ago, 2 years ago, < 3 years 69.05 (± 11.03) 54 (46.6%) Not in the past 3 years 65.62 (± 10.25) 64 (55.7%) Obstetric history Current gestational age groups 1st Trimester (10–13 weeks) 68.82 (± 12.67) 75 (45.7%) 0.822 2st Trimester (14–27 weeks) 68.97 (± 10.97) 49 (44.5%) 3st Trimester (28–41 weeks) 68.05 (± 10.38) 285 (47.4%) Planned Pregnancy Yes 70.33 (± 11.21) 212 (36.5%) < 0.001 No 64.32 (± 9.06) 197 (67.0%) Preconception care (n = 590) Yes 71.84 (± 12.05) 11 (33.5%) 0.072 No 68.19 (± 9.89) 102 (40.8%) Pregnancy surveillance 1–11 weeks 68.96 (± 11.05) 336 (43.3%) < 0.001 12–27 weeks 63.37 (± 8.21) 69 (72.6%) ≥ 28 weeks 58.55 (± 4.49) 4 (100%) Children(s) No 69.29 (± 11.04) 221 (44.0%) 0.065 Yes 67.00 (± 10.62) 188 (50.4%) Site of the queries Health center 64.47 (± 8.63) 182 (63.6%) < 0.001 Health center (HC) / Hospital 66.59 (± 11.03) 74 (59.7%) HC / Hospital/ Private clinic 69.10 (± 10.61) 33 (37.5%) Health center/ Private clinic 71.60 (± 11.45) 20 (31.8%) Pregnancy risk Yes 67.53 (± 10.65) 117 (51.5%) 0.092 No 68.58 (± 10.99) 292 (45.1%) Breastfeeding Yes 68.74 (± 10.86) 360 (44.5%) < 0.001 No 59.80 (± 7.72) 8 (72.7%) Cannot breastfeed 62.50 (± 9.45) 2 (50.0%) Has not yet decided 63.71 (± 10.64) 39 (76.5%) Childbirth preparation program Already attends 70.90 (± 11.60) 97 (34.5%) < 0.001 No 64.99 (± 8.34) 212 (60.9%) Has not yet decided / Cannot attend 65.42 (± 10.37) 53 (56.4%) Intends to attend 72.90 (± 12.21) 47 (30.9%) Footnotes : 1 Fisher’s exact or chi-square tests used to evaluate associations between limited health literacy and sociodemographic, health behaviors and lifestyles, health status, gynecologic and obstetric history variables. HL, health literacy; SD, standard deviation. Limited health literacy was more pronounced among specific subgroups, reflecting significant disparities across sociodemographic, behavioral, and health-related variables. Younger pregnant women, particularly those aged 18 to 29 years, had the highest prevalence of limited health literacy at 56.2%. Women born outside of Portugal exhibited a markedly higher prevalence (74.7%) compared to Portuguese nationals. Education level also strongly correlated with health literacy, as 90.9% of those with only primary education (2nd cycle) showed limited health literacy. Similarly, marital status revealed disparities, with divorced, separated, or widowed women reporting the highest prevalence at 92.3%. Occupational status highlighted further inequalities, with 80.8% of undifferentiated workers and 76.2% of those not professionally active demonstrating limited health literacy. Regarding housing conditions, 57.6% of women living in homes owned by relatives had limited health literacy, compared to lower rates among those renting or owning their homes. The absence of healthcare training played a critical role, as 51.3% of those without such training exhibited limited health literacy. Financial strain was a key determinant, with 61.3% of women who found it difficult to meet expenses reporting limited health literacy. Behavioral and lifestyle factors also played a role. Women who smoked occasionally had the highest prevalence of limited health literacy (71.9%), followed by those who quit smoking during pregnancy (60.5%). Exposure to tobacco smoke was associated with higher rates of limited health literacy (58.5%) compared to those not exposed. Occasional alcohol or psychoactive substance users had a prevalence of 62.3%. Among physical activity levels, the highest prevalence was observed in women who reported never engaging in physical activity (63.2%). Regarding dietary habits, limited health literacy was most common among those with occasional (75.0%) or light fruit and vegetable consumption (77.6%). Obesity prior to pregnancy was associated with 60.6% limited health literacy, followed by overweight women (46.7%). Self-perceived health status also revealed disparities. Women who rated their previous health as "bad or very bad" had the highest prevalence of limited health literacy (81.8%), while those who rated their current health as "much worse or worse" had a prevalence of 58.6%. In terms of gynecological history, 73.2% of women who had never undergone cervical cytology exhibited limited health literacy, as did 65.6% of those who did not use contraceptive methods. Pregnancy-related variables further emphasized disparities. Women in their third trimester (28–41 weeks) exhibited the highest prevalence of limited health literacy (47.4%) compared to those in the first (45.7%) and second trimesters (44.5%). Among women with unplanned pregnancies, 67.0% demonstrated limited health literacy, significantly higher than the 36.5% observed among those with planned pregnancies. Delayed initiation of pregnancy surveillance was strongly associated with limited health literacy, with 72.6% of those starting at 12–27 weeks affected and 100% of those starting surveillance at 28 weeks or later. Women without children had a lower prevalence of limited health literacy (44.0%) compared to those with children (50.4%). Healthcare engagement and intentions regarding breastfeeding and childbirth preparation programs further highlighted disparities. Women attending queries only at health centers had the highest prevalence of limited health literacy (63.6%) compared to 37.5% among those attending multiple types of healthcare facilities. Those undecided about breastfeeding (76.5%) or who did not intend to breastfeed (72.7%) exhibited higher rates of limited health literacy compared to women who intended to breastfeed (44.5%). Among those not attending childbirth preparation programs, 60.9% had limited health literacy, while only 30.9% of those intending to attend such programs were affected. Significant associations were found between sociodemographic factors, health behaviors and lifestyles, health status characteristics, gynecological history and obstetric history variables of the pregnant women. These included age, country of birth, education level, profession/occupation, employment status, work situation, type of residence, training in a healthcare profession, perceived financial difficulty, smoking behavior, exposure to second-hand smoke, consumption of alcohol or psychoactive substances, physical activity, fruit and vegetable intake, body mass index, previous and current self-perceived health status, management of chronic illness, health-related activity limitations, contraceptive use, cervical smear testing, number of children, weeks of antenatal care, site of antenatal care, breastfeeding decisions, and participation in childbirth preparation programmes. Determinants of Limited Health Literacy Variables showing statistical differences in limited health literacy across categories met the selection threshold for inclusion in the regression analysis (p < 0.10). Table 5 provides the crude and adjusted odds ratios (ORs) for limited health literacy, highlighting associations with key sociodemographic characteristics, health-related lifestyle behaviors, health indicators, gynecologic and obstetric history among the pregnant women, as derived from the binary logistic regression analyses. Table 5 Multivariate logistic regression analysis to predict limited general health literacy. Crude OR (95% CI) 1 Adjusted OR, 1st bloc (95% CI) 2 Adjusted OR, 2nd bloc (95% CI) 3 Age groups ≥ 40 years 1 - - 30–39 years 0.49 (0.28–0.85) - - 18–29 years 0.53 (0.40–0.71) - - Country of birth Portugal 1 1 1 Other country 4.39 (3.00-6.42) 2.44 (1.58–3.75) 2.43 (1.56–3.80) Educational level University education 1 - 1 High school 0.04 (0.01–0.14) - 0.20 (0.05–0.77) 3rd cycle 0.11 (0.03–0.37) - 0.23 (0.06–0.86) Up to 2nd cycle 0.22 (0.06–0.79) - 0.30 (0.08–1.15) Profession/occupation Intellectual and scientific 1 1 1 Techniques 0.08 (0.05–0.13) 0.25 (0.14–0.44) 0.36 (0.19–0.70) Undifferentiated 0.19 (0.12–0.29) 0.34 (0.22–0.54) 0.42 (0.25–0.68) Employment status Working professionally 1 - - Not working professionaly 5.12 (3.55–7.38) - - Payment of expenses at the end of the month Very easy/Easy 1 1 1 Very difficult/Difficult 5.34 (3.92–7.27) 3.24 (2.29–4.60) 2.87 (1.99–4.14) Planned pregnancy Yes 1 - 1 No 3.53 (2.62–4.75) - 1.37 (0.94-2.00) Surveillance pregnancy 1–11 weeks 1 - 1 ≥ 12 weeks 3.67 (2.29–5.88) - 0.95 (0.53–1.72) Previous Self-perceived health Good/Very good 1 1 1 Fair 0.14 (0.03–0.66) 0.21 (0.03–1.15) 0.26 (0.04–1.47) Bad/Very bad 0.39 (0.08–1.85) 0.34 (0.06–1.91) 0.39 (0.07–2.20) Current Selperceived health Better/Much Better 1 1 1 Equal 0.32 (0.16–0.62) 0.24 (0.11–0.51) 0.25 (0.11–0.54) Worse/much worse 0.61 (0.40–0.93) 0.61 (0.38–0.99) 0.65 (0.40–1.05) BMI prior to pregnancy Underweight 1 - 1 Normal weight 0.48 (0.23–0.98) - 0.36 (0.15–0.84) Overweight 0.49 (0.33–0.73) - 0.74 (0.47–1.18) Obesity 0.57 (0.36–0.88) - 0.67 (0.41–1.12) OR, odds ratio; CI, confidence interval. 1 Binary logistic regression model (univariate analyses, not adjusted). 2 Binary logistic regression model (forward, LR method) adjusted for age groups, country of birth, educational level, profession/occupation, employment status, payment of expenses at the end of the month, previous self-perceived health status, current self-perceived health status, planned pregnancy, pregnancy surveillance and BMI. 3 Binary logistic regression model (1st bloc: forward, LR method; 2nd bloc: enter method) adjusted for country of birth, educational level, profession/occupation, payment of expenses, planned pregnancy, surveillance pregnancy, previous self-perceived health, current self-perceived health, and BMI. In the first regression model (univariate, not adjusted), several variables show a significant relationship with limited health literacy. Regarding age groups, younger individuals demonstrate lower odds of limited literacy compared to those aged 40 and above. Those aged 18–29 years have 47% lower odds of limited literacy (Crude OR = 0.53, 95% CI: 0.40–0.71), while individuals aged 30–39 years have 51% lower odds (Crude OR = 0.49, 95% CI: 0.28–0.85). Country of birth is a significant factor, with individuals born outside Portugal having more than four times the odds of limited health literacy compared to Portuguese-born individuals (Crude OR = 4.39, 95% CI: 3.00–6.42). Education level is also crucial. Compared to university graduates, individuals with high school education have 96% lower odds of limited literacy (Crude OR = 0.04, 95% CI: 0.01–0.14), those with 3rd cycle education have 89% lower odds (Crude OR = 0.11, 95% CI: 0.03–0.37), and those with education up to the 2nd cycle have 78% lower odds (Crude OR = 0.22, 95% CI: 0.06–0.79). Regarding profession/occupation, compared to individuals in intellectual and scientific professions, those in technical professions have 92% lower odds of limited literacy (Crude OR = 0.08, 95% CI: 0.05–0.13), while individuals in undifferentiated professions have 81% lower odds (Crude OR = 0.19, 95% CI: 0.12–0.29). Employment status also shows a significant association with health literacy. Individuals not working professionally have more than five times the odds of limited literacy compared to those employed (Crude OR = 5.12, 95% CI: 3.55–7.38). Financial hardship is another key predictor. Those experiencing difficulty paying monthly expenses have more than five times the odds of limited literacy compared to individuals who find it easy to cover their expenses (Crude OR = 5.34, 95% CI: 3.92–7.27). Regarding pregnancy-related factors, individuals with an unplanned pregnancy have more than three times the odds of limited literacy compared to those with a planned pregnancy (Crude OR = 3.53, 95% CI: 2.62–4.75). Similarly, individuals who initiated pregnancy surveillance at ≥ 12 weeks have more than three times the odds of limited literacy (Crude OR = 3.67, 95% CI: 2.29–5.88). In terms of self-perceived health, individuals who currently rate their health as equal to before have 68% lower odds of limited literacy (Crude OR = 0.32, 95% CI: 0.16–0.62), while those who perceive their health as worse or much worse have 39% lower odds (Crude OR = 0.61, 95% CI: 0.40–0.93). For body mass index (BMI) prior to pregnancy, individuals with normal weight have 52% lower odds of limited literacy compared to those who are underweight (Crude OR = 0.48, 95% CI: 0.23–0.98). In a second regression approach (first block forward), country of birth remains a significant determinant, with individuals born outside Portugal exhibiting more than twice the odds of limited health literacy compared to those born in Portugal (Adjusted OR = 2.44, 95% CI: 1.58–3.75). Profession/occupation continues to demonstrate a strong association with health literacy, compared to individuals in intellectual and scientific professions, those in technical professions show 75% lower odds of limited literacy (Adjusted OR = 0.25, 95% CI: 0.14–0.44), while individuals in undifferentiated professions exhibit 66% lower odds (Adjusted OR = 0.34, 95% CI: 0.22–0.54). Financial difficulties remain a key predictor. Individuals who report difficulty covering monthly expenses have more than three times the odds of limited health literacy compared to those without financial strain (Adjusted OR = 3.24, 95% CI: 2.29–4.60). Previous self-perceived health does not show a statistically significant association after adjustment. Current self-perceived health continues to be a relevant factor. Compared to those who perceive their health as better or much better, individuals who rate their health as the same demonstrate 76% lower odds of limited literacy (Adjusted OR = 0.24, 95% CI: 0.11–0.51). Those who perceive their health as worse or much worse do not present a statistically significant association (Adjusted OR = 0.61, 95% CI: 0.38–0.99). BMI also becomes significant in this model. Compared to underweight pregnant women’s, those with normal weight exhibit 64% lower odds of limited literacy (Adjusted OR = 0.36, 95% CI: 0.15–0.84), however, overweight and obesity do not show statistically significant associations. In the final adjusted binary logistic regression model (1st bloc: forward, LR method; 2nd bloc: enter method) adjusted for country of birth, educational level, profession/occupation, payment of expenses, planned pregnancy, surveillance pregnancy, previous self-perceived health, current self-perceived health, and BMI remain significant determinants of limited health literacy among pregnant women. Country of birth continues to be a strong predictor, with pregnant women born outside Portugal exhibiting more than twice the odds of limited health literacy compared to those born in Portugal (Adjusted OR = 2.43, 95% CI: 1.56–3.80). Educational level remains a key determinant of health literacy. Compared to pregnant women with university education, those with high school education exhibit 80% lower odds of limited literacy (Adjusted OR = 0.20, 95% CI: 0.05–0.77), while those with 3rd cycle education has 77% lower odds (Adjusted OR = 0.23, 95% CI: 0.06–0.86). Education up to the 2nd cycle does not present a statistically significant association in the fully adjusted model. Profession/occupation continues to demonstrate an association with health literacy. Compared to pregnant women in intellectual and scientific professions, those in technical professions have 64% lower odds of limited literacy (Adjusted OR = 0.36, 95% CI: 0.19–0.70), while those in undifferentiated professions show 58% lower odds (Adjusted OR = 0.42, 95% CI: 0.25–0.68). Financial hardship remains a strong predictor. Pregnant women who report difficulty covering monthly expenses have nearly three times the odds of limited health literacy compared to those without financial strain (Adjusted OR = 2.87, 95% CI: 1.99–4.14). Planned pregnancy is no longer statistically significant in the fully adjusted model. Pregnant women with an unplanned pregnancy exhibit 37% higher odds of limited health literacy (Adjusted OR = 1.37, 95% CI: 0.94–2.00), but this association does not reach statistical significance. Surveillance pregnancy, defined as delayed initiation of prenatal care (≥ 12 weeks), also does not present a significant association with limited health literacy in the final model (Adjusted OR = 0.95, 95% CI: 0.53–1.72). Previous self-perceived health does not show a statistically significant association after adjustment, indicating that retrospective health perception may not be a strong determinant of health literacy. Current self-perceived health remains a relevant factor. Compared to those who perceive their health as better or much better, pregnant women who rate their current health as the same exhibit 75% lower odds of limited health literacy (Adjusted OR = 0.25, 95% CI: 0.11–0.54). However, those who consider their health as worse or much worse do not present a statistically significant association (Adjusted OR = 0.65, 95% CI: 0.40–1.05). Body Mass Index (BMI) prior to pregnancy continues to be an influential factor. Compared to underweight women, those with normal weight demonstrate 64% lower odds of limited health literacy (Adjusted OR = 0.36, 95% CI: 0.15–0.84). Overweight and obesity do not show significant associations in the final model. At last, the area under the ROC curve (AUC) for the final model is 0.782, indicating a good level of discriminatory power and suggests that the model has a 78.2% probability of correctly distinguishing between individuals with and without limited health literacy. This result demonstrates that the model reliably differentiates between the two categories, providing a strong basis for its predictive validity in this context. Discussion This study offers a comprehensive assessment of general health literacy (HL) among pregnant women from the district of Viseu, in Portugal, based on a large sample and using the HLS 19 -Q12, an internationally validated instrument. The distribution of general health literacy among pregnant women revealed that 4.2% were classified as having inadequate HL, 42.5% problematic, 42.5% sufficient, and 10.7% excellent. This profile differs from that observed in the Portuguese general population, where a nationally representative survey using the same HLS19-Q12 instrument found 7.5% inadequate, 22% problematic, 65% sufficient, and 5% excellent HL among adults aged 16 to 87 years, with a mean age of 46 years ( 19 ). Compared to these national estimates, the pregnant women in our sample, whose mean age was 31.1 years, exhibited a substantially higher proportion in the problematic category and a lower proportion in the sufficient category, despite showing a slightly higher percentage in the excellent group. This pattern may be partially explained by the sociodemographic characteristics of the sample. A significant proportion of participants reported financial difficulties, lower educational levels, and occupations in less qualified professional groups, all factors known to be associated with reduced health literacy across diverse populations ( 18 , 36 ). These social determinants may mitigate the potential benefits usually linked to younger age and regular contact with healthcare services during pregnancy. In contrast, the health literacy profile of older adults in Portugal appears even more limited: 25.4% were classified as inadequate, 55.2% problematic, 15.6% sufficient, and only 3.7% excellent, in a sample with a mean age of 72.8 years ( 21 ). These comparisons position pregnant women in an intermediate profile between general and older populations, underscoring the need to address literacy gaps even among younger adults facing additional structural and socioeconomic vulnerabilities. Building on these results, it is noteworthy that 46.7% of participants in this study were classified as having limited HL, either problematic or inadequate. This proportion places the sample at the upper end of the range reported in previous international studies conducted among pregnant women, where the prevalence of limited HL typically varies between 15% and 50%, depending on the instrument used, population characteristics, and socio-cultural context ( 3 , 7 ). Nearly 38% of women of reproductive age in Brazil were found to have problematic health literacy, as reported in a recent study using the HLS-EU-BR instrument ( 22 ), reinforcing the comparability of these findings across cultural and socioeconomic settings. Such figures are particularly concerning given the increased demand for information processing and decision-making during pregnancy, a period in which health-related knowledge and self-management capabilities are essential. Overall, this study found that while the majority of pregnant women reported positive health behaviors such as high fruit and vegetable consumption and engagement in physical activity and risk behaviors including tobacco and alcohol use, as well as physical inactivity, were still present among a significant proportion of participants. Approximately one in eight women continued smoking during pregnancy, and nearly one in ten reported occasional alcohol consumption, echoing national and international evidence showing that these behaviors persist despite well-known risks ( 3 , 23 – 25 ). Physical inactivity was reported by over 25% of participants, which is consistent with previous findings indicating suboptimal adherence to prenatal exercise recommendations ( 26 – 28 ). Conversely, nearly 90% of participants reported regular intake of fruits and vegetables, a pattern associated with improved pregnancy outcomes, including reduced risk of low birth weight and hypertensive complications ( 29 – 31 ). However, as shown in other studies, such dietary behaviors may be unequally distributed across subgroups, particularly among those with limited health literacy ( 3 , 30 ). In this sample, limited health literacy was more prevalent among women who smoked, were physically inactive, and reported no or minimal changes in health behaviors during pregnancy. Prior studies confirm that women with lower health literacy may have less access to reliable health information, lower self-efficacy in managing health, and reduced understanding of the risks associated with lifestyle behaviors such as smoking, alcohol intake, and inadequate diet ( 1 , 32 , 33 ). Furthermore, physical inactivity and excessive pre-pregnancy BMI were more frequent among those with limited HL, reinforcing the relationship between literacy levels, lifestyle management, and pregnancy-related risk profiles. Health literacy not only influences behavior directly but also mediates how individuals interpret, evaluate, and act on health advice received from professionals. As shown in several international reviews, HL plays a critical role in shaping maternal decision-making and autonomy, especially when managing multiple health messages during pregnancy ( 7 , 34 ). Beyond the overall prevalence, this study revealed specific patterns in the distribution of HL. Pregnant women reported more difficulties in accessing and appraising health information than in understanding it, patterns similar to those identified in the Portuguese general population ( 19 ). However, the presence of these difficulties even among women actively engaged with healthcare services suggests that current antenatal care encounters may not fully address HL needs. The disaggregated analysis showed marked inequalities in HL levels across various subgroups. Women born outside Portugal, those with lower levels of education, in lower-skilled occupations, or experiencing financial hardship were significantly more likely to have limited HL. For example, 61.3% of pregnant women who reported difficulty covering monthly expenses had limited health literacy, compared to only 22.9% among those without financial strain. Similarly, 76.2% of women who were not professionally active had limited health literacy, in contrast to 38.4% of those employed. These results reinforce the relationship between economic vulnerability, occupational exclusion, and reduced capacity to access, process, and apply health information during pregnancy, as documented in previous international research ( 3 , 5 , 7 , 35 ). Moreover, certain pregnancy-related characteristics, such as unplanned pregnancy and delayed initiation of antenatal care (≥ 12 weeks), were associated with limited HL in the univariate analysis, though these associations did not remain statistically significant after adjustment. This suggests that structural sociodemographic determinants may exert a stronger influence on HL than pregnancy-specific experiences. The final regression model confirmed that limited HL was independently associated with being born outside Portugal, lower educational attainment, occupational category, financial hardship, and current self-perceived health status. These findings highlight how individual and systemic factors converge to shape HL disparities. Notably, women who rated their current health as "the same" compared to before pregnancy had significantly lower odds of limited HL than those who rated it as worse, reinforcing the bidirectional relationship between HL and perceived well-being. Interestingly, neither maternal age nor educational level remained significant in the adjusted models in some analyses, which contrasts with findings from general population studies ( 36 ). This may be due to the relative homogeneity of age and schooling within the pregnant population, and the stronger mediating role of financial hardship and occupation. Similar observations were reported in a recent study, which found that financial insecurity could attenuate the effect of education in multivariable models ( 22 ). The results of this study confirm the importance of considering HL within a broader framework of social determinants of health, especially in prenatal care settings. They also support the perspective that pregnancy is a critical window for intervention, where HL-enhancing strategies can have a direct impact on maternal and child outcomes. The regression model presented an acceptable discriminatory capacity in identifying pregnant women at risk for limited HL, as shown by the area under the ROC curve, consistent with established criteria ( 37 ). While this supports the robustness of the statistical model, it is important to interpret associations cautiously given the cross-sectional nature of the data. Nevertheless, this study contributes to an emerging body of research emphasizing the complexity of HL and the necessity of tailoring interventions to population subgroups with specific needs. Implications for Maternal Healthcare The findings from this study have clear implications for maternal healthcare practice and policy. The high prevalence of limited HL among pregnant women, particularly in groups facing social and economic vulnerability, calls for the integration of HL-sensitive approaches within routine prenatal care. Healthcare professionals must be equipped with the skills to identify HL limitations and communicate accordingly, using plain language, visual supports, and confirmation techniques such as teach-back. In addition, prenatal care services should assess HL levels systematically to inform individualized care plans. Given the increasing role of digital platforms in accessing health information, particularly during pregnancy, digital health literacy must also be addressed. From a policy perspective, HL should be considered a central component of maternal health promotion strategies. Incorporating HL indicators into national health surveys and investing in community-level interventions that target the social determinants identified in this study could help reduce HL inequalities and improve maternal outcomes. Strengths and limitations This study presents several strengths. First, the use of the HLS 19 -Q12, a validated, comprehensive, and multidimensional instrument aligned with international standards, enhances the methodological rigor and allows for meaningful comparisons with studies in other countries. Second, the sampling strategy ensured representativeness of the pregnant population in the central region of Portugal, capturing a wide range of sociodemographic and obstetric characteristics. This diversity permitted a detailed exploration of health literacy across various subgroups, enhancing the interpretability and policy relevance of the findings. A further methodological strength was the implementation of a multimodal data collection strategy, whereby participants could respond to the survey online, through interviewer administration, or via self-completed paper questionnaires. This flexible approach increased accessibility for participants with differing digital skills, literacy levels, and personal preferences. Prior research has demonstrated that multimodal survey designs can improve response rates, reduce selection bias, enhance sample composition, and maintain or even improve data quality while controlling costs. These advantages are particularly relevant in studies involving pregnant women, where heterogeneity in socioeconomic and educational backgrounds is common. Nevertheless, it is important to acknowledge limitations this study. Although the cross-sectional design limits the ability to establish causal relationships between health literacy and its potential determinants, future qualitative work, such as focus groups with pregnant women, could provide valuable insight into the mechanisms and contextual factors underlying these associations. Although the study potentially achieved regional representativeness, given the large sample size, generalization to the national level should be made with caution, not least because representativeness has not been formally ascertained, and different regions may exhibit different social and health service dynamics. Conclusion The prevalence of limited general health literacy among pregnant women in this study is considerably high, with nearly half of the participants reporting difficulties in accessing, understanding, and using health information. After controlling for other variables, being born outside Portugal, having a lower level of education, holding a lower-skilled occupation, experiencing financial hardship, and perceiving one's current health as worse or unchanged were independently associated with limited health literacy. These findings emphasize the social gradient in health literacy during pregnancy and highlight the compounded disadvantage faced by socioeconomically vulnerable subgroups. The identification of key sociodemographic and health-related determinants of limited health literacy in this population reveals the urgent need to incorporate literacy-sensitive strategies into maternal healthcare. Addressing literacy disparities among pregnant women is particularly relevant, as inadequate health literacy may compromise engagement with antenatal care, self-care behaviors, and ultimately, maternal and neonatal outcomes. These results underscore the importance of adapting health communication and prenatal care services to the needs of women with lower health literacy. From a public health perspective, the findings reinforce the call for integrated health promotion strategies that explicitly consider health literacy as a determinant of maternal health. Policymakers and healthcare professionals should prioritize interventions that reduce barriers to health information and empower pregnant women with the knowledge and confidence to make informed decisions. In doing so, it will be possible to enhance health equity and contribute to improved health outcomes across the perinatal continuum. Abbreviations aOR Adjusted odds ratios AUC Area under the curve BMI Body Mass Index CL Confidence Limits cOR Crude odds rations HL Health Literacy HLS19-Q12 12-item version of the Health Literacy Survey from the Health HLS Health Literacy Survey OR odds ratio M-POHL Measuring Population and Organizational Health Literacy ROC Receiver Operating Characteristic SD Standard deviation WHO World Health Organization Declarations Acknowledgements The authors would like to thank all the people who generously participated in the study. Author’s contributions NF designed the study. NF and SF collected data. NF performed the analysis, and all the authors interpreted the data. NF wrote the main manuscript text. ES and NF used the software analysis. MF, ES, SF, MA, AC read, provided feedback on, and approved the final version of the manuscript. Funding Not applicable. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Informed consent was obtained from all participants before any procedure. The study was previously approved by the Institutional Health Ethics Committees of the Hospital Center Tondela-Viseu (reference 29/13/09/2023) and the Central Regional Health Administration (process number 124-2023). Furthermore, all procedures followed the Code of Ethics of the Declaration of Helsinki. Consent for publication Not applicable Competing interests None of the authors have a conflict of interest related to this study. Authorization to use the HLS 19 -Q12 instrument in this research was obtained from the HLS 19 Consortium at: link: https://m-pohl.net/Design_Methods. The instrument is free for non-commercial academic use, and the authors confirm compliance with all required conditions. 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Sewor C, Obeng AA, Eliason S, Agbeno EK, Amegah AK. Fruits and vegetables intake improves birth outcomes of women with gestational diabetes mellitus and hypertensive disorders of pregnancy. BMC Nutr. 2024 Jan 2;10(1):2. doi: 10.1186/s40795-023-00814-w. PMID: 38167235; PMCID: PMC10763264. Miyake Y, Tanaka K, Okubo H, Sasaki S, Arakawa M. Maternal consumption of vegetables, fruit, and antioxidants during pregnancy and risk for childhood behavioral problems. Nutrition. 2020 Jan;69:110572. doi: 10.1016/j.nut.2019.110572. Epub 2019 Aug 24. PMID: 31563826. Skreden M, Bere E, Sagedal LR, Vistad I, Øverby NC. Changes in fruit and vegetable consumption habits from pre-pregnancy to early pregnancy among Norwegian women. BMC Pregnancy Childbirth. 2017 Apr 4;17(1):107. doi: 10.1186/s12884-017-1291-y. PMID: 28376732; PMCID: PMC5381088. Easton P, Entwistle VA, Williams B. Health in the 'hidden population' of people with low literacy. A systematic review of the literature. BMC Public Health. 2010 Aug 5;10:459. doi: 10.1186/1471-2458-10-459. PMID: 20687946; PMCID: PMC2923110. Von Wagner C, Knight K, Steptoe A, Wardle J. Functional health literacy and health-promoting behaviour in a national sample of British adults. J Epidemiol Community Health. 2007 Dec;61(12):1086–90. McCormack L, Haun J, Sørensen K, Valerio M. Recommendations for advancing health literacy measurement. J Health Commun. 2013;18 Suppl 1(Suppl 1):9-14. doi: 10.1080/10810730.2013.829892. PMID: 24093340; PMCID: PMC3815143. Bello CB, Esan DT, Akerele SA, Fadare RI. Maternal health literacy, utilisation of maternal healthcare services and pregnancy outcomes among newly delivered mothers: A cross-sectional study in Nigeria. Public Health Pract (Oxf). 2022 May 3;3:100266. doi: 10.1016/j.puhip.2022.100266. PMID: 36101756; PMCID: PMC9461586. Sørensen K, Pelikan JM, Röthlin F, Ganahl K, Slonska Z, Doyle G, Fullam J, Kondilis B, Agrafiotis D, Uiters E, Falcon M, Mensing M, Tchamov K, van den Broucke S, Brand H; HLS-EU Consortium. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur J Public Health. 2015 Dec;25(6):1053-8. doi: 10.1093/eurpub/ckv043. Epub 2015 Apr 5. PMID: 25843827; PMCID: PMC4668324. Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd, editor. 2013. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6396883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453730111,"identity":"5a333d63-f52b-4f34-af2c-871d593eb76c","order_by":0,"name":"Nuno Ferreira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJCCDyCCjYGB8QGQ5uEjQgfjDKgWZgOQFjaitYB0SUD14gfmYocPNnzcc0+OT/rws8qvOXYybAzMDx/dwKPFcnZaYuOMZ8XGbHxpZrdltyUDHcZmbJyDR4vB7RzzxzwHEhLbeBjMbktuYwZq4WGTJqDFsPnPgYT6Nh72b8WS2+qJ1MJwICGBjYfHjPHjtsPEaAH6pedAgmEbD0+xNOO24zxszAT9knyw4ceBBHn5HvaNH39uq7bnZ29++BifFhTAzAMmiVUOAow/SFE9CkbBKBgFIwYAAOzMQ3RBzGFaAAAAAElFTkSuQmCC","orcid":"","institution":"Nursing Research, Innovation and Development Centre of Lisbon (CIDNUR), Nursing School of Lisbon, Av. 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Prof. Egas Moniz, Lisbon 1600-190","correspondingAuthor":false,"prefix":"","firstName":"Andreia","middleName":"","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2025-04-07 18:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6396883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6396883/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-24225-7","type":"published","date":"2025-08-29T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82603888,"identity":"d24cda3d-8a38-4610-8186-74e4bc6a2055","added_by":"auto","created_at":"2025-05-13 09:54:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40072,"visible":true,"origin":"","legend":"\u003cp\u003eLevels (%) of general health literacy.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6396883/v1/84074e4f975b2002f39bafc7.png"},{"id":82602189,"identity":"231e2d5f-9cb3-42b7-be86-43e5df068f63","added_by":"auto","created_at":"2025-05-13 09:46:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55586,"visible":true,"origin":"","legend":"\u003cp\u003eHealth Literacy domains (%).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6396883/v1/46d6dc97ba5bb83fa9fbb3cc.png"},{"id":82605515,"identity":"6212c2cc-47c2-4b3b-9184-e2d9cc7ca23f","added_by":"auto","created_at":"2025-05-13 10:02:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62286,"visible":true,"origin":"","legend":"\u003cp\u003eDimensions of health information processing (%).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6396883/v1/06a0cb4787ffc15d9408c686.png"},{"id":90344876,"identity":"254481d9-ce8b-4dac-b88b-0da00d0708ff","added_by":"auto","created_at":"2025-09-01 16:07:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2573447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6396883/v1/320e032c-6626-4ada-8677-ee3891685e10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Literacy and Its Determinants Among Pregnant Women in Portugal","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth literacy, defined as the ability to access, understand, appraise, and apply health-related information, is a critical determinant of health outcomes and equity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is particularly crucial during pregnancy, a period marked by heightened health demands and complex decision-making, impacting both maternal and child health outcomes. Pregnant women must navigate diverse sources of health information and collaborate effectively with healthcare providers, making health literacy an indispensable competency (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch highlights significant associations between low health literacy and adverse pregnancy outcomes, including gestational diabetes, low birth weight, and increased maternal stress (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, inadequate health literacy is linked to delays in accessing prenatal care, limited adoption of health-promoting behaviors, and lower engagement with antenatal education (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These challenges underscore the importance of addressing health literacy disparities, especially in vulnerable populations such as immigrants, socioeconomically disadvantaged groups, and women with limited educational backgrounds (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Migrant women, in particular, may face compounded vulnerabilities related to cultural, social, and structural barriers that limit access to adequate reproductive and maternal healthcare services, as highlighted in recent umbrella reviews focused on equity in sexual and reproductive health outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Studies have also shown that low health literacy affects healthcare utilization and adherence to medical recommendations, potentially exacerbating inequalities in maternal and neonatal health outcomes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe percentages of health literacy (HL) levels among pregnant women vary significantly across cultural, socioeconomic, and geographical contexts, as well as depending on the measurement tools used. International studies provide valuable insights into these variations. Globally, between 15% and 50% of pregnant women are classified as having inadequate or limited HL, with significant differences observed across countries and populations (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Approximately 33% demonstrate sufficient HL, while only 10\u0026ndash;20% achieve excellent HL levels (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For example, in the United States, 38% of pregnant women were found to have low HL, which was associated with poorer comprehension of prenatal tests and medical guidelines (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Similarly, in Turkey, 33.9% of pregnant women had sufficient HL, while 66% exhibited limited HL (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In Europe, a study from Denmark highlighted that immigrant pregnant women have lower HL levels compared to native-born women (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In Iran, 42.8% of pregnant women were found to have adequate HL, whereas 15.5% had inadequate HL (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe investigations specifically focused on health literacy during pregnancy remains limited, despite the significance of this population as a key target for public health interventions. Nonetheless, some initiatives have begun to explore the topic indirectly or within localized contexts, such as studies examining health behaviors during pregnancy. However, studies in Portugal with representative data, or large samples, on health literacy levels among pregnant women, as well as associated factors like sociodemographic determinants and cultural barriers, remain unavailable. For example, in a study of 404 Portuguese pregnant women, the authors found that 50.5% had limited health literacy (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In contrast, another study in Portugal, reported that only 25.8% of a sample of 264 pregnant women demonstrated limited health literacy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These variations underscore the impact of differing methodologies, contexts, and population characteristics on the outcomes of health literacy assessments. As emphasized by Zibellini et al., the design of interventions to improve health literacy must account not only for the accessibility of information but also for the cultural and socioeconomic barriers that impede effective healthcare utilization (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePregnancy represents a pivotal period in the life cycle, where individual, familial, and transgenerational factors converge from somatic, psychological, and cultural perspectives. It should be viewed not only as a phase leading to specific outcomes but also as a strategic window for interventions that promote lasting changes in habits and behaviors. These changes can benefit the woman, her child, and the entire family unit across the life span. Examples include adopting healthier eating habits, increasing physical activity, and cessation of smoking and psychoactive substance use. Furthermore, pregnancy offers a critical opportunity to identify and address conditions that may pose risks for future health complications, such as gestational diabetes and pre-eclampsia. Evidence now demonstrates that the presence of these conditions during pregnancy significantly elevates the long-term risk of developing chronic diseases, including diabetes and cardiovascular disorders, in women (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The concept of pregnancy as a \"teachable moment\" for improving health literacy (HL) is well-supported in the literature, emphasizing that women are often highly motivated to adopt health-promoting behaviors during this time. This period is seen as an opportunity to influence behaviors such as nutrition, physical activity, and healthcare engagement, benefiting both maternal and child health (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, the regular interaction with healthcare systems during pregnancy enhances the potential for targeted interventions aimed at fostering long-term health literacy and behavioral changes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to assess general health literacy among pregnant women in Portugal, focusing on its relationship with sociodemographic, socioeconomic, and healthcare-related factors. By analyzing levels of general health literacy in a large sample from the district of Viseu, this study seeks to identify determinants of limited health literacy, providing insights to support equitable access to prenatal care improving maternal and neonatal outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment\u003c/h2\u003e \u003cp\u003eThe World Health Organization\u0026rsquo;s report \u0026ldquo;Health Literacy: The Solid Facts\u0026rdquo; (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) emphasized the importance of regularly measuring health literacy (HL) across populations using standardized and theory-based approaches. This recommendation aligns with the HLS-EU conceptual framework, which defines HL comprehensively and provides validated measurement tools, such as the HLS-EU-Q47 and its shorter derivatives, the HLS-EU-Q16, HLS-EU-Q12, and HLS-EU-Q6 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Recognizing the need for high-quality, internationally comparable data on population and organizational HL, the WHO established the Action Network on Measuring Population and Organizational Health Literacy (M-POHL) in 2018 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on the foundation of the HLS-EU study, the HLS\u003csub\u003e19\u003c/sub\u003e survey aimed to assess general population HL comprehensively across participating countries. At a minimum, HL was measured using the HLS\u003csub\u003e19\u003c/sub\u003e-Q12, a validated short form. Additionally, the survey included optional modules to evaluate specific areas of HL: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) digital HL (8 items), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) communicative HL (long form with 11 items, short form with 6 items), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) navigational HL (12 items), and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) vaccination HL (4 items). These modules served to enhance the understanding of specific HL dimensions and validate the discriminant validity of the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Portuguese version of the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 has been validated in a representative sample of individuals aged 16 and older, including 643 women (52%) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), on the other hand, the HLS-EU study identified, on average, a weak but statistically significant β coefficient indicating that females tend to have higher health literacy scores than males. However, this relationship was not statistically significant in certain countries included in the HLS-EU analysis (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe scale evaluates three key domains of health literacy: health care, disease prevention, and health promotion, across four dimensions of health information processing: accessing, understanding, appraising, and applying health-related information. Participants rated the 12 items on a 4-point Likert scale, ranging from \"1\u0026thinsp;=\u0026thinsp;very difficult\" to \"4\u0026thinsp;=\u0026thinsp;very easy.\" The general health literacy score was calculated by summing the responses to all valid items and rescaling the total to a 0-100 scale, with higher scores indicating better health literacy (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Similarly, seven sub-indices corresponding to the three domains and four dimensions were calculated using the same summing and rescaling method. If fewer than 80% of the items required for a given calculation were valid, the respective score was deemed \"missing.\" The overall health literacy score and the seven sub-indices were categorized into four levels: \"inadequate\" (0\u0026ndash;50, inclusive), \"problematic\" (50\u0026ndash;66.67, inclusive), \"sufficient\" (66.67\u0026ndash;83.33, inclusive), and \"excellent\" (above 83.33). Additionally, a composite variable combining the \"problematic\" and \"inadequate\" levels was created to represent \"limited health literacy.\" While the original developers of the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 proposed two distinct methods for categorizing health literacy scores, the approach applied in this study was selected due to its broader acceptance (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Authorization to use the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 instrument in this research was obtained from the HLS\u003csub\u003e19\u003c/sub\u003e Consortium at: link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://m-pohl.net/Design_Methods\u003c/span\u003e\u003cspan address=\"https://m-pohl.net/Design_Methods\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTesting\u003c/h3\u003e\n\u003cp\u003e All participants provided written informed consent before any procedures were conducted, following prior ethical approval by the Institutional Health Ethics Committees of the Tondela Viseu Hospital Center (reference 13/29/09/2023) and the Central Regional Health Administration (process number 124\u0026ndash;2023). Additionally, all procedures adhered to the ethical principles outlined in the Declaration of Helsinki (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo facilitate participation and ensure convenience for pregnant women, the survey, which included the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 instrument, was offered in three formats to achieve a homogeneous sample. Participants could respond digitally via a QR code or link distributed during pregnancy appointments (28.8%), through an interview conducted in primary healthcare or hospital settings (45.4%), or by self-completing a paper-based version (25.8%). The latter option had a response rate of 76%. The inclusion criteria required participants to be at least 18 years old, at least 10 weeks pregnant, and able to read and comprehend Portuguese. No exclusion criteria were established. Data collection was carried out between the 15th of October 2023 and the 15th of May 2024.\u003c/p\u003e \u003cp\u003eBased on gestational ages at enrolment, the estimated delivery window for the surveyed sample spans and considering the average number of births recorded annually in the district of Viseu during this period, approximately 1.800 births per year, the 886 pregnant women who participated in the study represent approximately 49% of the expected pregnancies in the region. This level of participation supports the robustness and regional representativeness of the sample, strengthening the external validity and applicability of the findings to the local pregnant population.\u003c/p\u003e \u003cp\u003eSociodemographic characteristics, lifestyle-related health behaviors, health-related variables, and gynecological and obstetric histories of the pregnant women participants were collected at the outset of the survey. Collected sociodemographic variables included age, which was categorized into age groups (18\u0026ndash;29 years; 30\u0026ndash;39 years; \u0026ge; 40 years), nationality (Portugal; another country), formal education (up to 2nd cycle of primary education; 3rd cycle of primary education; high school; university education); marital status (single, married/common-law marriage; divorced/separated/widow; employment status (working professionally; not working professionally); profession/ occupation (intellectual and scientific; techniques; undifferentiated); residence typology (own house/apartment; rented house/apartment; social house/apartment); situation in the work (worker on their own; family worker; worker on account of other; domestic, student, unemployed or retired; residence typology (own house/apartment; rented house/apartment; home/apartment of relatives; social house/institution); training in a healthcare profession (yes; no).\u003c/p\u003e \u003cp\u003eWe collected data on health behaviors and lifestyles, including smoking status (never smoked; smoked before pregnancy; quit during pregnancy; or occasional smoking), exposure to tobacco smoke (yes or no), and consumption of alcohol or psychoactive substances (never; used before pregnancy; quit during pregnancy; or occasional use). Information on physical activity levels was also gathered, categorized as never, never due to medical restrictions, occasional, light, or heavy. Dietary habits were assessed through fruit and vegetable consumption (never; occasional use that correspond to less than one day per week and 1 day; 2 days and 3 days grouped as light use; 4 days, 5 days, 6 days, 7 days grouped as heavy). Additionally, participants reported their pre-pregnancy Body Mass Index (BMI), classified as underweight, normal weight, overweight, or obese.\u003c/p\u003e \u003cp\u003eHealth status variables were also included, such as prior self-perceived health status (categorized as bad or very bad; fair; good or very good) and current self-perceived health status (much worse or worse; equal; better or much better). Self-reported chronic diseases or disabilities were recorded (yes or no), along with the perceived ease of managing these conditions (categorized as easy or very easy; hard or very difficult). Finally, participants were asked whether health problems limited their daily activities (not limited; limited; greatly limited).\u003c/p\u003e \u003cp\u003eData on gynecological variables included the use of contraceptive methods (yes or no) and cervical cytology status, categorized as never performed, within the last 12 months, more than 1 year but less than 2 years ago, more than 2 years but less than 3 years ago, or not performed in the last 3 years. For obstetric history, variables collected encompassed current gestational age groups (10\u0026ndash;13 weeks, 14\u0026ndash;27 weeks, or 28\u0026ndash;41 weeks), attendance at preconception care consultations (yes or no), whether the pregnancy was planned (yes or no), the number of weeks pregnant at the time the pregnancy was discovered, and whether the pregnancy was classified as high-risk (yes or no). Additional information included the number of children, the location of antenatal care (health center, private clinic, or hospital), intention to breastfeed (yes, not yet decided, no, or cannot breastfeed), and intention to attend a childbirth preparation program (no, already attending, plans to attend, or undecided/cannot attend).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was carried out using IBM SPSS Statistics\u0026reg;, version 29.0 (IBM Corp, Armonk, NY, USA), with a 5% significance level. Descriptive statistics were used to summarize the sample's sociodemographic profile, lifestyle-related health behaviors, health-related characteristics, and gynecological and obstetric histories. The evaluation also included the computation of mean scores and standard deviations for overall health literacy and its sub-dimensions. Furthermore, the distribution of participants across the four health literacy categories and dichotomized variables was reported as percentages, based on valid responses. The number of respondents included in the scoring and the extent of missing data were also recorded.\u003c/p\u003e \u003cp\u003eBivariate analyses were conducted using Fisher\u0026rsquo;s exact test and chi-square tests to identify associations between health literacy levels and various factors. General health literacy, dichotomized into \u0026ldquo;limited\u0026rdquo; and \u0026ldquo;not limited,\u0026rdquo; served as the dependent variable in a binary logistic regression analysis. Independent variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in bivariate analyses were included as predictors of limited health literacy in the logistic regression. The first model applied a forward likelihood ratio (LR) approach, while a second model used an enter method to add variables of interest not selected in the initial analysis. Any variables related to age, gender, education, residence, or financial household status excluded from the first model were reintroduced in a subsequent block.\u003c/p\u003e \u003cp\u003eThe findings are presented as crude odds ratios (cOR) and adjusted odds ratios (aOR), along with 95% confidence intervals (95% CI). The residual probabilities of the final adjusted logistic regression model were utilized to calculate the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample and items description\u003c/h2\u003e \u003cp\u003eThe study included a sample of 886 pregnant women with a mean age of 31.09 years (\u0026plusmn;\u0026thinsp;5.60 years). The participants ranged in age from 18 to 51 years. More than half of the sample (53.3%) were aged between 30 and 39 years, 39.7% were between 18 and 29 years, and 7.0% were 40 years or older. Most participants were Portuguese nationals (81.2%), with 18.8% born in other countries. Educational attainment was relatively high, as 42.6% had completed university education, and 40.7% had finished high school. Regarding marital status, 73.4% were married or in a common-law relationship, while 25.2% were single. In terms of employment, 77.8% of the participants were professionally active. Among these, 52.5% were employed in technical occupations, 27.3% held intellectual or scientific roles, and 20.2% worked in undifferentiated professions. Additionally, 69.2% worked for others, 8.1% were self-employed, and 1.2% were family workers. However, 21.4% of participants were not engaged in professional work, including students, domestic, unemployed individuals, or retirees. Regarding housing, 46.4% owned their homes, 32.5% rented, and 20.8% lived with relatives, while 0.3% resided in social housing. Notably, 15.2% of participants reported having training in a healthcare profession, while 84.8% did not. The analysis of financial stress among participants reveals that a majority faced challenges in meeting their expenses. Specifically, 61.7% of participants reported finding it \"difficult\" or \"very difficult\" to pay their expenses, indicating a significant level of financial strain. Conversely, 38.3% of participants found paying expenses to be \"very easy\" or \"easy,\" suggesting a minority experienced financial ease. Further details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the sample (n\u0026thinsp;=\u0026thinsp;886)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;886\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.09\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Groups (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e472 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e719 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnother country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFormal education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd cycle of primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd cycle of primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/common-law marriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e650 (73.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking professionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working professionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfession/ occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual and scientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechniques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence typology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn house/apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e411 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRented house/apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatives house/apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial house/apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSituation in the work\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker on their own\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker on account of other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e613 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic_Student_Unemploymen_retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraining in a healthcare profession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e751 (84.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePayment of expenses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Easy_easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e339 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficult_ Very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (61.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e SD, Standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eHealth behaviors and lifestyles and health status were examined to better understand factors influencing maternal health. Regarding smoking behavior, 74.0% of participants reported never smoking, while 13.9% smoked before pregnancy, 4.9% quit during pregnancy, and 7.2% smoked occasionally. Tobacco smoke exposure was reported by 32.3% of participants. Alcohol or psychoactive substance use were reported by 33.2% of pregnant women. Among these, 16.8% consumed substances before pregnancy, 7.6% quit during pregnancy, and 8.8% used them occasionally. Physical activity levels varied, with 34.9% engaging in light activity, 30.2% in heavy activity, and 25.4% reporting no physical activity. Fruit and vegetable consumption were notably high, with 89.4% reporting heavy intake. Pre-pregnancy BMI classifications revealed that 55.9% had a normal BMI, 24.5% were overweight, and 15.0% were classified as obese. Regarding self-perceived health, 69.8% rated their previous health as good or very good, while 29.0% considered it fair, and 1.2% described it as bad or very bad. During pregnancy, 11.2% felt their health had worsened, while 81.7% reported no change, and 7.1% indicated improvement. Chronic diseases or disabilities were reported by 19.2% of participants, with 30.6% finding it difficult to manage these conditions. Additionally, 12.8% experienced no activity limitations, 5.5% reported some limitations, and 0.9% faced significant limitations. Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHealth behaviors and lifestyles and health status variables of the sample (n\u0026thinsp;=\u0026thinsp;886)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth behaviors and lifestyles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking behavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e656 (74.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStopped in pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco smoke exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol or psychoative subs. (PS) consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e592 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStopped in pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever: medical restriction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFruit and vegetable consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e792 (89.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI prior to pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495 (55.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Self-perceived health status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad or very bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood or very good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent Self-perceived health status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuch worse or worse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEqual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e724 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetter or much better\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious self-reported chronic (SRC) disease/disability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e716 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDealing with SRC disease/disability (n\u0026thinsp;=\u0026thinsp;170)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEasy or too easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHard or very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth problems limited the activity (n\u0026thinsp;=\u0026thinsp;170)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot limited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreatly limited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e SD, Standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study also examined gynecological and obstetric variables to provide insights into participants' health profiles and pregnancy care (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regarding the use of contraceptive methods, 89.7% of participants reported using contraception, while 10.3% did not. Cervical cytology results showed that 12.8% had never undergone the procedure. Among those who had, 36.6% underwent it within the last 12 months, 24.4% between one and two years ago, 13.3% between two and three years ago, and 13.0% more than three years ago. The mean gestational age was 29.60 weeks (\u0026plusmn;\u0026thinsp;10.61). Most participants were in their third trimester (28\u0026ndash;41 weeks; 68.3%), followed by the first trimester (10\u0026ndash;13 weeks; 19.1%) and the second trimester (14\u0026ndash;27 weeks; 12.6%). Preconception care was reported by 57.3% of participants, while 42.7% did not attend such consultations. Planned pregnancies accounted for 66.6% of cases, while 33.4% of pregnancies were unplanned. In terms of pregnancy surveillance, the majority (88.8%) initiated care between 1 and 11 weeks of gestation, 10.7% began care between 12 and 27 weeks, and 0.5% started care at 28 weeks or later. Pregnancy risk were identified in 25.8% of participants, while 74.2% report a low risk pregnancy. Regarding parity, 57.4% of participants were experiencing their first pregnancy, while 42.6% had one or more children, with a mean of 0.57 children (\u0026plusmn;\u0026thinsp;4.21). Antenatal care sites varied, with 43.3% receiving care at both health centers and private clinics, 32.5% exclusively at health centers, 14.1% at health centers and hospitals, and 10.0% across health centers, hospitals, and private clinics. When asked about breastfeeding intentions, 92.6% of participants intended to breastfeed, 5.8% were undecided, 1.2% did not intend to breastfeed, and 0.5% reported being unable to breastfeed. Regarding childbirth preparation programs, 39.4% of participants did not attend, 32.1% were already attending, 17.6% intended to attend, and 10.9% had not yet decided or could not attend, probably because they are at risk of premature birth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGynecologic and obstetric history variables of the sample (n\u0026thinsp;=\u0026thinsp;886)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynecologic history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraceptive method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e795 (89.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervix vaginal cytology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn the last 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1 year ago, \u0026lt; 2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 years ago, \u0026lt; 3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot in the past 3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObstetric history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent gestational age\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.60\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent gestational age groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Trimester (10\u0026ndash;13 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2st Trimester (14\u0026ndash;27 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3st Trimester (28\u0026ndash;41 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e605 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreconception care (n\u0026thinsp;=\u0026thinsp;590)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanned pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590 (66.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy surveillance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;11 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e787 (88.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;27 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e657 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildren(s)\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildren(s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e509 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite of the queries\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center / Private clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center / Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center / Hospital / Private clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBreastfeeding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e820 (92.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas not yet decided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot breastfeed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildbirth preparation program\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlready attends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntends to attend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas not yet decided/ Cannot attend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e SD, Standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of Limited Health Literacy Across Sample Subgroups\u003c/h2\u003e \u003cp\u003eThe distribution of limited health literacy across sample subgroups were calculated using data from participants, ranging from 856 for the \"Access\" dimension of health information processing to 885 for \"Health Promotion,\" a domain of health literacy. Specifically, the general health literacy score was based on 875 participants, while the domain scores included 875 for \"Healthcare\" and 879 for \"Disease Prevention.\" For the dimensions of health information processing, the number of participants was 880 for \"Understand,\" 883 for \"Appraise,\" and 885 for \"Apply.\" The variation in sample size reflects the number of pregnant women who provided at least 80% of valid responses required for the calculation of each specific score.\u003c/p\u003e \u003cp\u003eA mean general health literacy score of 68.31 (\u0026plusmn;\u0026thinsp;10.92) was observed, which was lower than the mean scores for each of the six sub-indexes. These ranged from 68.32 (\u0026plusmn;\u0026thinsp;16.77) in the \u0026ldquo;Access\u0026rdquo; dimension of health information processing to 72.50 (\u0026plusmn;\u0026thinsp;11.30) in the \u0026ldquo;Apply\u0026rdquo; domain of health information processing. An exception was noted in the \u0026ldquo;Disease Prevention\u0026rdquo; domain, which exhibited a slightly lower mean score of 67.48 (\u0026plusmn;\u0026thinsp;12.83).\u003c/p\u003e \u003cp\u003eThe distribution of general health literacy (HL) among pregnant women, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, shows a substantial proportion of participants with intermediate levels of HL. Specifically, 4.2% were classified as having inadequate HL and 42.5% as problematic, indicating that nearly half of the sample experienced difficulties in accessing, understanding, or using health information. An equal proportion of participants (42.5%) demonstrated sufficient HL, suggesting functional skills to manage health-related tasks and decision-making during pregnancy. Only 10.7% of participants were classified in the excellent category, reflecting a relatively small subgroup with the highest levels of autonomy and confidence in dealing with health information.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of health literacy across the three health information domains (Healthcare, Disease Prevention, and Health Promotion) reveals domain-specific patterns in the distribution of HL levels among pregnant women. In the Healthcare domain, 6.2% of participants were classified as having inadequate HL and 24.8% as problematic, while 55.1% reported sufficient HL and 13.9% excellent. These findings suggest that most women felt confident navigating the healthcare system and interacting with medical professionals, possibly due to their regular contact with antenatal care services. In the Disease Prevention domain, HL levels shifted slightly, with 7.1% of participants classified as inadequate and 33.1% as problematic. Meanwhile, 47.1% reported sufficient HL and 12.7% excellent. This domain presented the highest proportion of participants in the problematic category, indicating potential difficulties in understanding or applying preventive information, such as vaccinations, screenings, or behavioral risk avoidance strategies. The most favorable distribution was observed in the Health Promotion domain, where only 3.2% of participants had inadequate HL and 15.7% problematic, while 66.1% were classified as sufficient and 15.0% as excellent. These results suggest that participants were more confident in engaging with information aimed at maintaining or improving general well-being, such as nutrition, physical activity, or stress management during pregnancy. Overall, the data indicate that while healthcare navigation appears to be relatively well-managed, there are important gaps in understanding and acting upon preventive strategies. Strengthening communication related to disease prevention may help reduce risk behaviors and improve maternal and fetal health outcomes (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHealth literacy performance in each of the four dimensions of information processing (access, understand, appraise, and apply) demonstrates distinct patterns. The access dimension presented the highest proportion of participants with inadequate HL (13.0%) and also the highest problematic HL level (25.5%), indicating that locating and obtaining health-related information may be the most challenging task for many pregnant women. In contrast, the apply dimension showed the lowest percentage of inadequate HL (4.6%) and problematic HL (9.9%), while presenting the highest proportion of sufficient HL (72.8%) and a relatively elevated excellent level (12.7%). This suggests that once information is understood, most participants felt capable of integrating it into their health decisions. For the understand dimension, 11.1% of participants were classified as inadequate, 15.8% as problematic, 63.3% as sufficient, and 9.3% as excellent. Regarding the appraise dimension, 8.2% were inadequate, 16.9% problematic, 63.6% sufficient, and 11.3% excellent. These results suggest that while comprehension and judgement of health information are generally adequate, they remain more complex than direct application. Overall, the data indicate that challenges are most prominent in the early stages of processing health information, particularly in access and appraisal, whereas applying information is reported more confidently. This may reflect the structured support provided in antenatal care but also highlights potential weaknesses in information navigation and evaluation skills during pregnancy (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence of Limited Health Literacy Across Participant Subgroups\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the findings from combining the \u0026ldquo;Inadequate\u0026rdquo; and \u0026ldquo;Problematic\u0026rdquo; categories of general health literacy into a new construct termed \u0026ldquo;Limited Health Literacy.\u0026rdquo; Among pregnant women with a minimum of 80% valid responses to the 12 items of the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 instrument (n\u0026thinsp;=\u0026thinsp;875), 46.7% were identified as having limited health literacy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral health literacy score means and limited health literacy by sociodemographic, health behaviors and lifestyles, health status, gynecologic and obstetric history characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral HL\u003c/p\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited HL\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003eValue \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.31 (\u0026plusmn;\u0026thinsp;10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Groups (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.36 (\u0026plusmn;\u0026thinsp;10.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (56,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.50 (\u0026plusmn;\u0026thinsp;11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (40,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.37 (\u0026plusmn;\u0026thinsp;11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (38,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.31 (\u0026plusmn;\u0026thinsp;11.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnother country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.03 (\u0026plusmn;\u0026thinsp;7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFormal education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd cycle of primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.21 (\u0026plusmn;\u0026thinsp;7.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd cycle of primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.98 (\u0026plusmn;\u0026thinsp;9.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 69.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.47 (\u0026plusmn;\u0026thinsp;9.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (53,1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e72.31 (\u0026plusmn;\u0026thinsp;11.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (29,6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.05 (\u0026plusmn;\u0026thinsp;9.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/common-law marriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.90 (\u0026plusmn;\u0026thinsp;11.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e60.12 (\u0026plusmn;\u0026thinsp;5.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfession/ occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual and scientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.67 (\u0026plusmn;\u0026thinsp;11.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechniques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.03 (\u0026plusmn;\u0026thinsp;10.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e61.77 (\u0026plusmn;\u0026thinsp;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking professionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.87 (\u0026plusmn;\u0026thinsp;11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (38,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working professionaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.78 (\u0026plusmn;\u0026thinsp;8.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (76,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSituation in the work\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker on their own\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.60 (\u0026plusmn;\u0026thinsp;11.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.05 (\u0026plusmn;\u0026thinsp;8.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker on account of other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.98 (\u0026plusmn;\u0026thinsp;10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic_Student_Unemploymen_retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.54 (\u0026plusmn;\u0026thinsp;8.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence typology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn house/ apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.64 (\u0026plusmn;\u0026thinsp;11.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (35,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRented house/apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.01 (\u0026plusmn;\u0026thinsp;9.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (55,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome/ apartment of relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.41 (\u0026plusmn;\u0026thinsp;10.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (57,6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial house/Institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.89 (\u0026plusmn;\u0026thinsp;16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraining in a healthcare profession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e76.49 (\u0026plusmn;\u0026thinsp;12.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (21,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.83 (\u0026plusmn;\u0026thinsp;9.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (51,3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePayment of expenses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Easy_easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.30 (\u0026plusmn;\u0026thinsp;10.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficult_ Very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.26 (\u0026plusmn;\u0026thinsp;9.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCont.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral HL\u003c/p\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited HL\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003eValue \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth behaviors and lifestyles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking behavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.47 (\u0026plusmn;\u0026thinsp;10.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.67 (\u0026plusmn;\u0026thinsp;11.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStopped in pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.58 (\u0026plusmn;\u0026thinsp;10.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e72.22 (\u0026plusmn;\u0026thinsp;7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco smoke exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.23 (\u0026plusmn;\u0026thinsp;10.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.31 (\u0026plusmn;\u0026thinsp;11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243 (41.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol or PS consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.20 (\u0026plusmn;\u0026thinsp;10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.90 (\u0026plusmn;\u0026thinsp;11.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (50.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStopped in pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e71.49 (\u0026plusmn;\u0026thinsp;12.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.31 (\u0026plusmn;\u0026thinsp;11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.89 (\u0026plusmn;\u0026thinsp;9.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever: medical restriction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e71.83 (\u0026plusmn;\u0026thinsp;7.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.93 (\u0026plusmn;\u0026thinsp;14.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.25 (\u0026plusmn;\u0026thinsp;10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.82 (\u0026plusmn;\u0026thinsp;10.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (39.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFruit and vegetable consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e71.92 (\u0026plusmn;\u0026thinsp;16.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.89 (\u0026plusmn;\u0026thinsp;9.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.81 (\u0026plusmn;\u0026thinsp;9.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (77.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.94 (\u0026plusmn;\u0026thinsp;10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (Body Mass Index) prior to pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.10 (\u0026plusmn;\u0026thinsp;9.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.44 (\u0026plusmn;\u0026thinsp;11.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.58 (\u0026plusmn;\u0026thinsp;10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.66 (\u0026plusmn;\u0026thinsp;9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (60.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Self-perceived health status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad or very bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e61.00 (\u0026plusmn;\u0026thinsp;37.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.87 (\u0026plusmn;\u0026thinsp;9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (63.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood or very good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.87 (\u0026plusmn;\u0026thinsp;11.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent Self-perceived health status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuch worse ou worse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.82 (\u0026plusmn;\u0026thinsp;11.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEqual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.08 (\u0026plusmn;\u0026thinsp;10.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetter or much better\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.42 (\u0026plusmn;\u0026thinsp;14.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious self-reported chronic disease or disability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.46 (\u0026plusmn;\u0026thinsp;10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.27 (\u0026plusmn;\u0026thinsp;11.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDealing with self-reported chronic disease (n\u0026thinsp;=\u0026thinsp;170)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEasy or too easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.25 (\u0026plusmn;\u0026thinsp;10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHard or very difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.70 (\u0026plusmn;\u0026thinsp;9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth problems have limited the activity (n\u0026thinsp;=\u0026thinsp;170)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot limited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.04 (\u0026plusmn;\u0026thinsp;10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.81 (\u0026plusmn;\u0026thinsp;10.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreatly limited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.14 (\u0026plusmn;\u0026thinsp;8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cem\u003eCont.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral HL\u003c/p\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited HL\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003eValue \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGynecologic History\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraceptive method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.67 (\u0026plusmn;\u0026thinsp;11.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.11 (\u0026plusmn;\u0026thinsp;8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (65.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervic vaginal cytology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.38 (\u0026plusmn;\u0026thinsp;8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn the last 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.33 (\u0026plusmn;\u0026thinsp;11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1 year ago, \u0026lt; 2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.44 (\u0026plusmn;\u0026thinsp;10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 years ago, \u0026lt; 3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.05 (\u0026plusmn;\u0026thinsp;11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot in the past 3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.62 (\u0026plusmn;\u0026thinsp;10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (55.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObstetric history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent gestational age groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Trimester (10\u0026ndash;13 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.82 (\u0026plusmn;\u0026thinsp;12.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2st Trimester (14\u0026ndash;27 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.97 (\u0026plusmn;\u0026thinsp;10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3st Trimester (28\u0026ndash;41 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.05 (\u0026plusmn;\u0026thinsp;10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanned Pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.33 (\u0026plusmn;\u0026thinsp;11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.32 (\u0026plusmn;\u0026thinsp;9.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreconception care (n\u0026thinsp;=\u0026thinsp;590)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e71.84 (\u0026plusmn;\u0026thinsp;12.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.19 (\u0026plusmn;\u0026thinsp;9.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy surveillance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;11 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.96 (\u0026plusmn;\u0026thinsp;11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;27 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.37 (\u0026plusmn;\u0026thinsp;8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.55 (\u0026plusmn;\u0026thinsp;4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildren(s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.29 (\u0026plusmn;\u0026thinsp;11.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.00 (\u0026plusmn;\u0026thinsp;10.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSite of the queries\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.47 (\u0026plusmn;\u0026thinsp;8.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center (HC) / Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.59 (\u0026plusmn;\u0026thinsp;11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC / Hospital/ Private clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.10 (\u0026plusmn;\u0026thinsp;10.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth center/ Private clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e71.60 (\u0026plusmn;\u0026thinsp;11.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.53 (\u0026plusmn;\u0026thinsp;10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.58 (\u0026plusmn;\u0026thinsp;10.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBreastfeeding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e68.74 (\u0026plusmn;\u0026thinsp;10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e59.80 (\u0026plusmn;\u0026thinsp;7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot breastfeed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.50 (\u0026plusmn;\u0026thinsp;9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas not yet decided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.71 (\u0026plusmn;\u0026thinsp;10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildbirth preparation program\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlready attends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e70.90 (\u0026plusmn;\u0026thinsp;11.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.99 (\u0026plusmn;\u0026thinsp;8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas not yet decided / Cannot attend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.42 (\u0026plusmn;\u0026thinsp;10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntends to attend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e72.90 (\u0026plusmn;\u0026thinsp;12.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eFootnotes\u003c/b\u003e: \u003csup\u003e1\u003c/sup\u003e Fisher\u0026rsquo;s exact or chi-square tests used to evaluate associations between limited health literacy and sociodemographic, health behaviors and lifestyles, health status, gynecologic and obstetric history variables. HL, health literacy; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLimited health literacy was more pronounced among specific subgroups, reflecting significant disparities across sociodemographic, behavioral, and health-related variables. Younger pregnant women, particularly those aged 18 to 29 years, had the highest prevalence of limited health literacy at 56.2%. Women born outside of Portugal exhibited a markedly higher prevalence (74.7%) compared to Portuguese nationals. Education level also strongly correlated with health literacy, as 90.9% of those with only primary education (2nd cycle) showed limited health literacy. Similarly, marital status revealed disparities, with divorced, separated, or widowed women reporting the highest prevalence at 92.3%. Occupational status highlighted further inequalities, with 80.8% of undifferentiated workers and 76.2% of those not professionally active demonstrating limited health literacy. Regarding housing conditions, 57.6% of women living in homes owned by relatives had limited health literacy, compared to lower rates among those renting or owning their homes. The absence of healthcare training played a critical role, as 51.3% of those without such training exhibited limited health literacy. Financial strain was a key determinant, with 61.3% of women who found it difficult to meet expenses reporting limited health literacy.\u003c/p\u003e \u003cp\u003eBehavioral and lifestyle factors also played a role. Women who smoked occasionally had the highest prevalence of limited health literacy (71.9%), followed by those who quit smoking during pregnancy (60.5%). Exposure to tobacco smoke was associated with higher rates of limited health literacy (58.5%) compared to those not exposed. Occasional alcohol or psychoactive substance users had a prevalence of 62.3%. Among physical activity levels, the highest prevalence was observed in women who reported never engaging in physical activity (63.2%). Regarding dietary habits, limited health literacy was most common among those with occasional (75.0%) or light fruit and vegetable consumption (77.6%). Obesity prior to pregnancy was associated with 60.6% limited health literacy, followed by overweight women (46.7%).\u003c/p\u003e \u003cp\u003eSelf-perceived health status also revealed disparities. Women who rated their previous health as \"bad or very bad\" had the highest prevalence of limited health literacy (81.8%), while those who rated their current health as \"much worse or worse\" had a prevalence of 58.6%. In terms of gynecological history, 73.2% of women who had never undergone cervical cytology exhibited limited health literacy, as did 65.6% of those who did not use contraceptive methods.\u003c/p\u003e \u003cp\u003ePregnancy-related variables further emphasized disparities. Women in their third trimester (28\u0026ndash;41 weeks) exhibited the highest prevalence of limited health literacy (47.4%) compared to those in the first (45.7%) and second trimesters (44.5%). Among women with unplanned pregnancies, 67.0% demonstrated limited health literacy, significantly higher than the 36.5% observed among those with planned pregnancies. Delayed initiation of pregnancy surveillance was strongly associated with limited health literacy, with 72.6% of those starting at 12\u0026ndash;27 weeks affected and 100% of those starting surveillance at 28 weeks or later. Women without children had a lower prevalence of limited health literacy (44.0%) compared to those with children (50.4%). Healthcare engagement and intentions regarding breastfeeding and childbirth preparation programs further highlighted disparities. Women attending queries only at health centers had the highest prevalence of limited health literacy (63.6%) compared to 37.5% among those attending multiple types of healthcare facilities. Those undecided about breastfeeding (76.5%) or who did not intend to breastfeed (72.7%) exhibited higher rates of limited health literacy compared to women who intended to breastfeed (44.5%). Among those not attending childbirth preparation programs, 60.9% had limited health literacy, while only 30.9% of those intending to attend such programs were affected.\u003c/p\u003e \u003cp\u003eSignificant associations were found between sociodemographic factors, health behaviors and lifestyles, health status characteristics, gynecological history and obstetric history variables of the pregnant women. These included age, country of birth, education level, profession/occupation, employment status, work situation, type of residence, training in a healthcare profession, perceived financial difficulty, smoking behavior, exposure to second-hand smoke, consumption of alcohol or psychoactive substances, physical activity, fruit and vegetable intake, body mass index, previous and current self-perceived health status, management of chronic illness, health-related activity limitations, contraceptive use, cervical smear testing, number of children, weeks of antenatal care, site of antenatal care, breastfeeding decisions, and participation in childbirth preparation programmes.\u003c/p\u003e\n\u003ch3\u003eDeterminants of Limited Health Literacy\u003c/h3\u003e\n\u003cp\u003eVariables showing statistical differences in limited health literacy across categories met the selection threshold for inclusion in the regression analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides the crude and adjusted odds ratios (ORs) for limited health literacy, highlighting associations with key sociodemographic characteristics, health-related lifestyle behaviors, health indicators, gynecologic and obstetric history among the pregnant women, as derived from the binary logistic regression analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis to predict limited general health literacy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003cp\u003e(95% CI) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR, 1st bloc (95% CI) \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR, 2nd bloc (95% CI) \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.28\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.40\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry of birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39 (3.00-6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44 (1.58\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43 (1.56\u0026ndash;3.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.01\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.05\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.03\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.06\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 2nd cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.06\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.08\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfession/occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual and scientific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechniques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.05\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.14\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36 (0.19\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.12\u0026ndash;0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.22\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.25\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking professionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working professionaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.12 (3.55\u0026ndash;7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePayment of expenses at the end of the month\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery easy/Easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery difficult/Difficult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34 (3.92\u0026ndash;7.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24 (2.29\u0026ndash;4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.87 (1.99\u0026ndash;4.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanned pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53 (2.62\u0026ndash;4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.94-2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurveillance pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;11 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;12 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.67 (2.29\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.53\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Self-perceived health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood/Very good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14 (0.03\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (0.03\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (0.04\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad/Very bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.08\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.06\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.07\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent Selperceived health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetter/Much Better\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEqual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.16\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24 (0.11\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25 (0.11\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorse/much worse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.40\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.38\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.40\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI prior to pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.23\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36 (0.15\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.33\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.47\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.36\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.41\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR, odds ratio; CI, confidence interval. \u003csup\u003e1\u003c/sup\u003eBinary logistic regression model (univariate analyses, not adjusted). \u003csup\u003e2\u003c/sup\u003eBinary logistic regression model (forward, LR method) adjusted for age groups, country of birth, educational level, profession/occupation, employment status, payment of expenses at the end of the month, previous self-perceived health status, current self-perceived health status, planned pregnancy, pregnancy surveillance and BMI. \u003csup\u003e3\u003c/sup\u003eBinary logistic regression model (1st bloc: forward, LR method; 2nd bloc: enter method) adjusted for country of birth, educational level, profession/occupation, payment of expenses, planned pregnancy, surveillance pregnancy, previous self-perceived health, current self-perceived health, and BMI.\u003c/p\u003e \u003cp\u003eIn the first regression model (univariate, not adjusted), several variables show a significant relationship with limited health literacy. Regarding age groups, younger individuals demonstrate lower odds of limited literacy compared to those aged 40 and above. Those aged 18\u0026ndash;29 years have 47% lower odds of limited literacy (Crude OR\u0026thinsp;=\u0026thinsp;0.53, 95% CI: 0.40\u0026ndash;0.71), while individuals aged 30\u0026ndash;39 years have 51% lower odds (Crude OR\u0026thinsp;=\u0026thinsp;0.49, 95% CI: 0.28\u0026ndash;0.85). Country of birth is a significant factor, with individuals born outside Portugal having more than four times the odds of limited health literacy compared to Portuguese-born individuals (Crude OR\u0026thinsp;=\u0026thinsp;4.39, 95% CI: 3.00\u0026ndash;6.42). Education level is also crucial. Compared to university graduates, individuals with high school education have 96% lower odds of limited literacy (Crude OR\u0026thinsp;=\u0026thinsp;0.04, 95% CI: 0.01\u0026ndash;0.14), those with 3rd cycle education have 89% lower odds (Crude OR\u0026thinsp;=\u0026thinsp;0.11, 95% CI: 0.03\u0026ndash;0.37), and those with education up to the 2nd cycle have 78% lower odds (Crude OR\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.06\u0026ndash;0.79). Regarding profession/occupation, compared to individuals in intellectual and scientific professions, those in technical professions have 92% lower odds of limited literacy (Crude OR\u0026thinsp;=\u0026thinsp;0.08, 95% CI: 0.05\u0026ndash;0.13), while individuals in undifferentiated professions have 81% lower odds (Crude OR\u0026thinsp;=\u0026thinsp;0.19, 95% CI: 0.12\u0026ndash;0.29). Employment status also shows a significant association with health literacy. Individuals not working professionally have more than five times the odds of limited literacy compared to those employed (Crude OR\u0026thinsp;=\u0026thinsp;5.12, 95% CI: 3.55\u0026ndash;7.38). Financial hardship is another key predictor. Those experiencing difficulty paying monthly expenses have more than five times the odds of limited literacy compared to individuals who find it easy to cover their expenses (Crude OR\u0026thinsp;=\u0026thinsp;5.34, 95% CI: 3.92\u0026ndash;7.27). Regarding pregnancy-related factors, individuals with an unplanned pregnancy have more than three times the odds of limited literacy compared to those with a planned pregnancy (Crude OR\u0026thinsp;=\u0026thinsp;3.53, 95% CI: 2.62\u0026ndash;4.75). Similarly, individuals who initiated pregnancy surveillance at \u0026ge;\u0026thinsp;12 weeks have more than three times the odds of limited literacy (Crude OR\u0026thinsp;=\u0026thinsp;3.67, 95% CI: 2.29\u0026ndash;5.88). In terms of self-perceived health, individuals who currently rate their health as equal to before have 68% lower odds of limited literacy (Crude OR\u0026thinsp;=\u0026thinsp;0.32, 95% CI: 0.16\u0026ndash;0.62), while those who perceive their health as worse or much worse have 39% lower odds (Crude OR\u0026thinsp;=\u0026thinsp;0.61, 95% CI: 0.40\u0026ndash;0.93). For body mass index (BMI) prior to pregnancy, individuals with normal weight have 52% lower odds of limited literacy compared to those who are underweight (Crude OR\u0026thinsp;=\u0026thinsp;0.48, 95% CI: 0.23\u0026ndash;0.98).\u003c/p\u003e \u003cp\u003eIn a second regression approach (first block forward), country of birth remains a significant determinant, with individuals born outside Portugal exhibiting more than twice the odds of limited health literacy compared to those born in Portugal (Adjusted OR\u0026thinsp;=\u0026thinsp;2.44, 95% CI: 1.58\u0026ndash;3.75). Profession/occupation continues to demonstrate a strong association with health literacy, compared to individuals in intellectual and scientific professions, those in technical professions show 75% lower odds of limited literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.25, 95% CI: 0.14\u0026ndash;0.44), while individuals in undifferentiated professions exhibit 66% lower odds (Adjusted OR\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.22\u0026ndash;0.54). Financial difficulties remain a key predictor. Individuals who report difficulty covering monthly expenses have more than three times the odds of limited health literacy compared to those without financial strain (Adjusted OR\u0026thinsp;=\u0026thinsp;3.24, 95% CI: 2.29\u0026ndash;4.60). Previous self-perceived health does not show a statistically significant association after adjustment. Current self-perceived health continues to be a relevant factor. Compared to those who perceive their health as better or much better, individuals who rate their health as the same demonstrate 76% lower odds of limited literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.24, 95% CI: 0.11\u0026ndash;0.51). Those who perceive their health as worse or much worse do not present a statistically significant association (Adjusted OR\u0026thinsp;=\u0026thinsp;0.61, 95% CI: 0.38\u0026ndash;0.99). BMI also becomes significant in this model. Compared to underweight pregnant women\u0026rsquo;s, those with normal weight exhibit 64% lower odds of limited literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.36, 95% CI: 0.15\u0026ndash;0.84), however, overweight and obesity do not show statistically significant associations.\u003c/p\u003e \u003cp\u003eIn the final adjusted binary logistic regression model (1st bloc: forward, LR method; 2nd bloc: enter method) adjusted for country of birth, educational level, profession/occupation, payment of expenses, planned pregnancy, surveillance pregnancy, previous self-perceived health, current self-perceived health, and BMI remain significant determinants of limited health literacy among pregnant women. Country of birth continues to be a strong predictor, with pregnant women born outside Portugal exhibiting more than twice the odds of limited health literacy compared to those born in Portugal (Adjusted OR\u0026thinsp;=\u0026thinsp;2.43, 95% CI: 1.56\u0026ndash;3.80). Educational level remains a key determinant of health literacy. Compared to pregnant women with university education, those with high school education exhibit 80% lower odds of limited literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.05\u0026ndash;0.77), while those with 3rd cycle education has 77% lower odds (Adjusted OR\u0026thinsp;=\u0026thinsp;0.23, 95% CI: 0.06\u0026ndash;0.86). Education up to the 2nd cycle does not present a statistically significant association in the fully adjusted model. Profession/occupation continues to demonstrate an association with health literacy. Compared to pregnant women in intellectual and scientific professions, those in technical professions have 64% lower odds of limited literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.36, 95% CI: 0.19\u0026ndash;0.70), while those in undifferentiated professions show 58% lower odds (Adjusted OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.25\u0026ndash;0.68). Financial hardship remains a strong predictor. Pregnant women who report difficulty covering monthly expenses have nearly three times the odds of limited health literacy compared to those without financial strain (Adjusted OR\u0026thinsp;=\u0026thinsp;2.87, 95% CI: 1.99\u0026ndash;4.14). Planned pregnancy is no longer statistically significant in the fully adjusted model. Pregnant women with an unplanned pregnancy exhibit 37% higher odds of limited health literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 0.94\u0026ndash;2.00), but this association does not reach statistical significance. Surveillance pregnancy, defined as delayed initiation of prenatal care (\u0026ge;\u0026thinsp;12 weeks), also does not present a significant association with limited health literacy in the final model (Adjusted OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.53\u0026ndash;1.72). Previous self-perceived health does not show a statistically significant association after adjustment, indicating that retrospective health perception may not be a strong determinant of health literacy. Current self-perceived health remains a relevant factor. Compared to those who perceive their health as better or much better, pregnant women who rate their current health as the same exhibit 75% lower odds of limited health literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.25, 95% CI: 0.11\u0026ndash;0.54). However, those who consider their health as worse or much worse do not present a statistically significant association (Adjusted OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI: 0.40\u0026ndash;1.05). Body Mass Index (BMI) prior to pregnancy continues to be an influential factor. Compared to underweight women, those with normal weight demonstrate 64% lower odds of limited health literacy (Adjusted OR\u0026thinsp;=\u0026thinsp;0.36, 95% CI: 0.15\u0026ndash;0.84). Overweight and obesity do not show significant associations in the final model.\u003c/p\u003e \u003cp\u003eAt last, the area under the ROC curve (AUC) for the final model is 0.782, indicating a good level of discriminatory power and suggests that the model has a 78.2% probability of correctly distinguishing between individuals with and without limited health literacy. This result demonstrates that the model reliably differentiates between the two categories, providing a strong basis for its predictive validity in this context.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers a comprehensive assessment of general health literacy (HL) among pregnant women from the district of Viseu, in Portugal, based on a large sample and using the HLS\u003csub\u003e19\u003c/sub\u003e-Q12, an internationally validated instrument. The distribution of general health literacy among pregnant women revealed that 4.2% were classified as having inadequate HL, 42.5% problematic, 42.5% sufficient, and 10.7% excellent. This profile differs from that observed in the Portuguese general population, where a nationally representative survey using the same HLS19-Q12 instrument found 7.5% inadequate, 22% problematic, 65% sufficient, and 5% excellent HL among adults aged 16 to 87 years, with a mean age of 46 years (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Compared to these national estimates, the pregnant women in our sample, whose mean age was 31.1 years, exhibited a substantially higher proportion in the problematic category and a lower proportion in the sufficient category, despite showing a slightly higher percentage in the excellent group. This pattern may be partially explained by the sociodemographic characteristics of the sample. A significant proportion of participants reported financial difficulties, lower educational levels, and occupations in less qualified professional groups, all factors known to be associated with reduced health literacy across diverse populations (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These social determinants may mitigate the potential benefits usually linked to younger age and regular contact with healthcare services during pregnancy. In contrast, the health literacy profile of older adults in Portugal appears even more limited: 25.4% were classified as inadequate, 55.2% problematic, 15.6% sufficient, and only 3.7% excellent, in a sample with a mean age of 72.8 years (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These comparisons position pregnant women in an intermediate profile between general and older populations, underscoring the need to address literacy gaps even among younger adults facing additional structural and socioeconomic vulnerabilities.\u003c/p\u003e \u003cp\u003eBuilding on these results, it is noteworthy that 46.7% of participants in this study were classified as having limited HL, either problematic or inadequate. This proportion places the sample at the upper end of the range reported in previous international studies conducted among pregnant women, where the prevalence of limited HL typically varies between 15% and 50%, depending on the instrument used, population characteristics, and socio-cultural context (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Nearly 38% of women of reproductive age in Brazil were found to have problematic health literacy, as reported in a recent study using the HLS-EU-BR instrument (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), reinforcing the comparability of these findings across cultural and socioeconomic settings. Such figures are particularly concerning given the increased demand for information processing and decision-making during pregnancy, a period in which health-related knowledge and self-management capabilities are essential.\u003c/p\u003e \u003cp\u003eOverall, this study found that while the majority of pregnant women reported positive health behaviors such as high fruit and vegetable consumption and engagement in physical activity and risk behaviors including tobacco and alcohol use, as well as physical inactivity, were still present among a significant proportion of participants. Approximately one in eight women continued smoking during pregnancy, and nearly one in ten reported occasional alcohol consumption, echoing national and international evidence showing that these behaviors persist despite well-known risks (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Physical inactivity was reported by over 25% of participants, which is consistent with previous findings indicating suboptimal adherence to prenatal exercise recommendations (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Conversely, nearly 90% of participants reported regular intake of fruits and vegetables, a pattern associated with improved pregnancy outcomes, including reduced risk of low birth weight and hypertensive complications (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, as shown in other studies, such dietary behaviors may be unequally distributed across subgroups, particularly among those with limited health literacy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sample, limited health literacy was more prevalent among women who smoked, were physically inactive, and reported no or minimal changes in health behaviors during pregnancy. Prior studies confirm that women with lower health literacy may have less access to reliable health information, lower self-efficacy in managing health, and reduced understanding of the risks associated with lifestyle behaviors such as smoking, alcohol intake, and inadequate diet (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Furthermore, physical inactivity and excessive pre-pregnancy BMI were more frequent among those with limited HL, reinforcing the relationship between literacy levels, lifestyle management, and pregnancy-related risk profiles. Health literacy not only influences behavior directly but also mediates how individuals interpret, evaluate, and act on health advice received from professionals. As shown in several international reviews, HL plays a critical role in shaping maternal decision-making and autonomy, especially when managing multiple health messages during pregnancy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond the overall prevalence, this study revealed specific patterns in the distribution of HL. Pregnant women reported more difficulties in accessing and appraising health information than in understanding it, patterns similar to those identified in the Portuguese general population (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, the presence of these difficulties even among women actively engaged with healthcare services suggests that current antenatal care encounters may not fully address HL needs. The disaggregated analysis showed marked inequalities in HL levels across various subgroups. Women born outside Portugal, those with lower levels of education, in lower-skilled occupations, or experiencing financial hardship were significantly more likely to have limited HL. For example, 61.3% of pregnant women who reported difficulty covering monthly expenses had limited health literacy, compared to only 22.9% among those without financial strain. Similarly, 76.2% of women who were not professionally active had limited health literacy, in contrast to 38.4% of those employed. These results reinforce the relationship between economic vulnerability, occupational exclusion, and reduced capacity to access, process, and apply health information during pregnancy, as documented in previous international research (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, certain pregnancy-related characteristics, such as unplanned pregnancy and delayed initiation of antenatal care (\u0026ge;\u0026thinsp;12 weeks), were associated with limited HL in the univariate analysis, though these associations did not remain statistically significant after adjustment. This suggests that structural sociodemographic determinants may exert a stronger influence on HL than pregnancy-specific experiences. The final regression model confirmed that limited HL was independently associated with being born outside Portugal, lower educational attainment, occupational category, financial hardship, and current self-perceived health status. These findings highlight how individual and systemic factors converge to shape HL disparities. Notably, women who rated their current health as \"the same\" compared to before pregnancy had significantly lower odds of limited HL than those who rated it as worse, reinforcing the bidirectional relationship between HL and perceived well-being.\u003c/p\u003e \u003cp\u003eInterestingly, neither maternal age nor educational level remained significant in the adjusted models in some analyses, which contrasts with findings from general population studies (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This may be due to the relative homogeneity of age and schooling within the pregnant population, and the stronger mediating role of financial hardship and occupation. Similar observations were reported in a recent study, which found that financial insecurity could attenuate the effect of education in multivariable models (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The results of this study confirm the importance of considering HL within a broader framework of social determinants of health, especially in prenatal care settings. They also support the perspective that pregnancy is a critical window for intervention, where HL-enhancing strategies can have a direct impact on maternal and child outcomes.\u003c/p\u003e \u003cp\u003eThe regression model presented an acceptable discriminatory capacity in identifying pregnant women at risk for limited HL, as shown by the area under the ROC curve, consistent with established criteria (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). While this supports the robustness of the statistical model, it is important to interpret associations cautiously given the cross-sectional nature of the data. Nevertheless, this study contributes to an emerging body of research emphasizing the complexity of HL and the necessity of tailoring interventions to population subgroups with specific needs.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Maternal Healthcare\u003c/h2\u003e \u003cp\u003eThe findings from this study have clear implications for maternal healthcare practice and policy. The high prevalence of limited HL among pregnant women, particularly in groups facing social and economic vulnerability, calls for the integration of HL-sensitive approaches within routine prenatal care. Healthcare professionals must be equipped with the skills to identify HL limitations and communicate accordingly, using plain language, visual supports, and confirmation techniques such as teach-back. In addition, prenatal care services should assess HL levels systematically to inform individualized care plans. Given the increasing role of digital platforms in accessing health information, particularly during pregnancy, digital health literacy must also be addressed. From a policy perspective, HL should be considered a central component of maternal health promotion strategies. Incorporating HL indicators into national health surveys and investing in community-level interventions that target the social determinants identified in this study could help reduce HL inequalities and improve maternal outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study presents several strengths. First, the use of the HLS\u003csub\u003e19\u003c/sub\u003e-Q12, a validated, comprehensive, and multidimensional instrument aligned with international standards, enhances the methodological rigor and allows for meaningful comparisons with studies in other countries. Second, the sampling strategy ensured representativeness of the pregnant population in the central region of Portugal, capturing a wide range of sociodemographic and obstetric characteristics. This diversity permitted a detailed exploration of health literacy across various subgroups, enhancing the interpretability and policy relevance of the findings. A further methodological strength was the implementation of a multimodal data collection strategy, whereby participants could respond to the survey online, through interviewer administration, or via self-completed paper questionnaires. This flexible approach increased accessibility for participants with differing digital skills, literacy levels, and personal preferences. Prior research has demonstrated that multimodal survey designs can improve response rates, reduce selection bias, enhance sample composition, and maintain or even improve data quality while controlling costs. These advantages are particularly relevant in studies involving pregnant women, where heterogeneity in socioeconomic and educational backgrounds is common.\u003c/p\u003e \u003cp\u003eNevertheless, it is important to acknowledge limitations this study. Although the cross-sectional design limits the ability to establish causal relationships between health literacy and its potential determinants, future qualitative work, such as focus groups with pregnant women, could provide valuable insight into the mechanisms and contextual factors underlying these associations. Although the study potentially achieved regional representativeness, given the large sample size, generalization to the national level should be made with caution, not least because representativeness has not been formally ascertained, and different regions may exhibit different social and health service dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prevalence of limited general health literacy among pregnant women in this study is considerably high, with nearly half of the participants reporting difficulties in accessing, understanding, and using health information. After controlling for other variables, being born outside Portugal, having a lower level of education, holding a lower-skilled occupation, experiencing financial hardship, and perceiving one's current health as worse or unchanged were independently associated with limited health literacy. These findings emphasize the social gradient in health literacy during pregnancy and highlight the compounded disadvantage faced by socioeconomically vulnerable subgroups.\u003c/p\u003e \u003cp\u003eThe identification of key sociodemographic and health-related determinants of limited health literacy in this population reveals the urgent need to incorporate literacy-sensitive strategies into maternal healthcare. Addressing literacy disparities among pregnant women is particularly relevant, as inadequate health literacy may compromise engagement with antenatal care, self-care behaviors, and ultimately, maternal and neonatal outcomes. These results underscore the importance of adapting health communication and prenatal care services to the needs of women with lower health literacy.\u003c/p\u003e \u003cp\u003eFrom a public health perspective, the findings reinforce the call for integrated health promotion strategies that explicitly consider health literacy as a determinant of maternal health. Policymakers and healthcare professionals should prioritize interventions that reduce barriers to health information and empower pregnant women with the knowledge and confidence to make informed decisions. In doing so, it will be possible to enhance health equity and contribute to improved health outcomes across the perinatal continuum.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted odds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Limits\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrude odds rations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Literacy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLS19-Q12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e12-item version of the Health Literacy Survey from the Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Literacy Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM-POHL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMeasuring Population and Organizational Health Literacy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the people who generously participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNF designed the study. NF and SF collected data. NF performed the analysis, and all the authors interpreted the data. NF wrote the main manuscript text. ES and NF used the software analysis. MF, ES, SF, MA, AC read, provided feedback on, and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants before any procedure. The study was previously approved by the Institutional Health Ethics Committees of the Hospital Center Tondela-Viseu (reference 29/13/09/2023) and the Central Regional Health Administration (process number 124-2023). Furthermore, all procedures followed the Code of Ethics of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors have a conflict of interest related to this study. Authorization to use the HLS\u003csub\u003e19\u003c/sub\u003e-Q12 instrument in this research was obtained from the HLS\u003csub\u003e19\u003c/sub\u003e Consortium at: link: https://m-pohl.net/Design_Methods. The instrument is free for non-commercial academic use, and the authors confirm compliance with all required conditions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eS\u0026oslash;rensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, Brand H; (HLS-EU) Consortium Health Literacy Project European. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. 2012 Jan 25;12:80. doi: 10.1186/1471-2458-12-80. PMID: 22276600; PMCID: PMC3292515.\u003c/li\u003e\n \u003cli\u003eMeldgaard M, Gamborg M, Terkildsen Maindal H. Health literacy levels among women in the prenatal period: A systematic review. Sex Reprod Healthc. 2022 Dec;34:100796. doi: 10.1016/j.srhc.2022.100796. Epub 2022 Nov 15. PMID: 36413879.\u003c/li\u003e\n \u003cli\u003eNawabi F, Krebs F, Vennedey V, Shukri A, Lorenz L, Stock S. Health Literacy in Pregnant Women: A Systematic Review. 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Vienna; 2021.\u003c/li\u003e\n \u003cli\u003eArriaga M, Francisco R, Nogueira P, Oliveira J, Silva C, C\u0026acirc;mara G, S\u0026oslash;rensen K, Dietscher C, Costa A. Health Literacy in Portugal: Results of the Health Literacy Population Survey Project 2019-2021. Int J Environ Res Public Health. 2022 Apr 1;19(7):4225. doi: 10.3390/ijerph19074225. PMID: 35409905; PMCID: PMC8998262.\u003c/li\u003e\n \u003cli\u003eWorld Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013 Nov 27;310(20):2191-4. doi: 10.1001/jama.2013.281053. PMID: 24141714.\u003c/li\u003e\n \u003cli\u003eCosta A, Feteira-Santos R, Alarc\u0026atilde;o V, Henriques A, Madeira T, Virgolino A, Arriaga M, Nogueira PJ. Health Literacy among Older Adults in Portugal and Associated Sociodemographic, Health and Healthcare-Related Factors. Int J Environ Res Public Health. 2023 Feb 25;20(5):4172. doi: 10.3390/ijerph20054172. PMID: 36901182; PMCID: PMC10002045.\u003c/li\u003e\n \u003cli\u003eCosta MS, Cordeiro VMC, Maia MAG, L\u0026ocirc;bo NRA, C\u0026acirc;ndido EL, Moreira MRC. Literacia para a sa\u0026uacute;de de mulheres em idade reprodutiva. Revista Remecs [Internet]. 26 de dezembro de 2023 [cited 5/01/2025];8(14):147-58. Available from: http://revistaremecs.com.br/index.php/remecs/article/view/1412\u003c/li\u003e\n \u003cli\u003eKipling L, Bombard J, Wang X, Cox S. Cigarette Smoking Among Pregnant Women During the Perinatal Period: Prevalence and Health Care Provider Inquiries - Pregnancy Risk Assessment Monitoring System, United States, 2021. MMWR Morb Mortal Wkly Rep. 2024 May 2;73(17):393-398. doi: 10.15585/mmwr.mm7317a2. PMID: 38696343; PMCID: PMC11065467.\u003c/li\u003e\n \u003cli\u003eTarasi B, Cornuz J, Clair C, Baud D. Cigarette smoking during pregnancy and adverse perinatal outcomes: a cross-sectional study over 10 years. 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PMID: 34478617.\u003c/li\u003e\n \u003cli\u003ePhysical Activity and Exercise During Pregnancy and the Postpartum Period: ACOG Committee Opinion, Number 804. Obstet Gynecol. 2020 Apr;135(4):e178-e188. doi: 10.1097/AOG.0000000000003772. PMID: 32217980.\u003c/li\u003e\n \u003cli\u003eSewor C, Obeng AA, Eliason S, Agbeno EK, Amegah AK. Fruits and vegetables intake improves birth outcomes of women with gestational diabetes mellitus and hypertensive disorders of pregnancy. BMC Nutr. 2024 Jan 2;10(1):2. doi: 10.1186/s40795-023-00814-w. PMID: 38167235; PMCID: PMC10763264.\u003c/li\u003e\n \u003cli\u003eMiyake Y, Tanaka K, Okubo H, Sasaki S, Arakawa M. Maternal consumption of vegetables, fruit, and antioxidants during pregnancy and risk for childhood behavioral problems. Nutrition. 2020 Jan;69:110572. doi: 10.1016/j.nut.2019.110572. Epub 2019 Aug 24. PMID: 31563826.\u003c/li\u003e\n \u003cli\u003eSkreden M, Bere E, Sagedal LR, Vistad I, \u0026Oslash;verby NC. Changes in fruit and vegetable consumption habits from pre-pregnancy to early pregnancy among Norwegian women. BMC Pregnancy Childbirth. 2017 Apr 4;17(1):107. doi: 10.1186/s12884-017-1291-y. PMID: 28376732; PMCID: PMC5381088.\u003c/li\u003e\n \u003cli\u003eEaston P, Entwistle VA, Williams B. Health in the \u0026apos;hidden population\u0026apos; of people with low literacy. A systematic review of the literature. BMC Public Health. 2010 Aug 5;10:459. doi: 10.1186/1471-2458-10-459. PMID: 20687946; PMCID: PMC2923110.\u003c/li\u003e\n \u003cli\u003eVon Wagner C, Knight K, Steptoe A, Wardle J. Functional health literacy and health-promoting behaviour in a national sample of British adults. J Epidemiol Community Health. 2007 Dec;61(12):1086\u0026ndash;90.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMcCormack L, Haun J, S\u0026oslash;rensen K, Valerio M. Recommendations for advancing health literacy measurement. J Health Commun. 2013;18 Suppl 1(Suppl 1):9-14. doi: 10.1080/10810730.2013.829892. PMID: 24093340; PMCID: PMC3815143.\u003c/li\u003e\n \u003cli\u003eBello CB, Esan DT, Akerele SA, Fadare RI. Maternal health literacy, utilisation of maternal healthcare services and pregnancy outcomes among newly delivered mothers: A cross-sectional study in Nigeria. Public Health Pract (Oxf). 2022 May 3;3:100266. doi: 10.1016/j.puhip.2022.100266. PMID: 36101756; PMCID: PMC9461586.\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;rensen K, Pelikan JM, R\u0026ouml;thlin F, Ganahl K, Slonska Z, Doyle G, Fullam J, Kondilis B, Agrafiotis D, Uiters E, Falcon M, Mensing M, Tchamov K, van den Broucke S, Brand H; HLS-EU Consortium. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur J Public Health. 2015 Dec;25(6):1053-8. doi: 10.1093/eurpub/ckv043. Epub 2015 Apr 5. PMID: 25843827; PMCID: PMC4668324.\u003c/li\u003e\n \u003cli\u003eHosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd, editor. 2013.\u0026nbsp;\u003c/li\u003e\n\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Literacy, Health Inequities, Pregnant Women, Maternal Health, Prenatal Care, Social Determinants of Health, Public Health","lastPublishedDoi":"10.21203/rs.3.rs-6396883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6396883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth literacy is a key determinant of health outcomes and equity, particularly during pregnancy, a period marked by increased information needs and critical health decisions. Despite its importance, data on health literacy among pregnant women in Portugal remain scarce. This study aimed to assess general health literacy levels and their associations with sociodemographic, health-related, and pregnancy-specific factors in a large sample of pregnant women from the district of Viseu, Portugal.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted with 886 pregnant women aged 18 years or older, using the validated HLS\u003csub\u003e19\u003c/sub\u003e-Q12 instrument to measure general health literacy. Data collection occurred between October 2023 and May 2024 using a multimodal approach (online, interview, and paper-based). Health literacy was categorized into four levels and also dichotomized as limited versus not limited. Descriptive statistics, bivariate analyses, and binary logistic regression were performed to identify determinants of limited health literacy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean general health literacy score was 68.3 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9). A total of 46.7% of participants were classified as having limited health literacy. Higher prevalence was observed among women aged 18\u0026ndash;29, those born outside Portugal, with lower education, in undifferentiated professions, and facing financial hardship. In the final adjusted logistic regression model, significant predictors of limited health literacy included being born outside Portugal (adjusted OR 2.43; 95% CI: 1.56\u0026ndash;3.80), having lower education (up to high school), holding lower-skilled occupations, financial difficulties, and rating current health as equal or worse. Body Mass Index prior to pregnancy was also associated with literacy levels. The model showed good discriminatory ability (area under the ROC curve\u0026thinsp;=\u0026thinsp;0.78).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA considerable proportion of pregnant women demonstrated limited health literacy, especially among socioeconomically vulnerable groups. These findings highlight the need for literacy-sensitive prenatal care practices and targeted public health interventions that address both individual and structural determinants. Incorporating health literacy assessments and tailored communication strategies in antenatal care could support informed decision-making, promote equity, improve maternal and neonatal outcomes.\u003c/p\u003e","manuscriptTitle":"Health Literacy and Its Determinants Among Pregnant Women in Portugal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 09:46:05","doi":"10.21203/rs.3.rs-6396883/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-12T11:22:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T20:00:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T13:52:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131217584253564213928881446402523737179","date":"2025-05-16T12:07:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173723672402066935965114874642705524554","date":"2025-05-14T03:58:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T22:48:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-11T10:20:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-10T01:20:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T01:19:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-07T18:43:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abfb56df-e7ac-4ef0-b486-a079739748c0","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:00:42+00:00","versionOfRecord":{"articleIdentity":"rs-6396883","link":"https://doi.org/10.1186/s12889-025-24225-7","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-08-29 15:57:28","publishedOnDateReadable":"August 29th, 2025"},"versionCreatedAt":"2025-05-13 09:46:05","video":"","vorDoi":"10.1186/s12889-025-24225-7","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24225-7","workflowStages":[]},"version":"v1","identity":"rs-6396883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6396883","identity":"rs-6396883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}