Leveraging Sociodemographic, Digital Exposure, and Knowledge-Based Empowerment Factors to Improve Maternal Health Behaviour Among Married Women in Nigeria: A Cross-Sectional Analysis of the 2018 NDHS

Leveraging Sociodemographic, Digital Exposure, and Knowledge-Based Empowerment Factors to Improve Maternal Health Behaviour Among Married Women in Nigeria: A Cross-Sectional Analysis of the 2018 NDHS | 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 Leveraging Sociodemographic, Digital Exposure, and Knowledge-Based Empowerment Factors to Improve Maternal Health Behaviour Among Married Women in Nigeria: A Cross-Sectional Analysis of the 2018 NDHS Muyiwa Oladosun, Anthony Etim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9246834/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Maternal health behaviour is a key determinant of maternal and child health outcomes in Nigeria, yet disparities in the utilisation of essential services persist. While socioeconomic factors remain important predictors, the role of digital exposure and knowledge-based empowerment in shaping women’s health-seeking behaviour is increasingly recognised. This study examines the influence of sociodemographic characteristics, digital exposure, and knowledge-based empowerment on maternal health behaviour among married women in Nigeria, with implications for achieving Sustainable Development Goals (SDGs) 3 and 5. Methods This study analysed cross-sectional data from the 2018 Nigeria Demographic and Health Survey (NDHS). The sample comprised 28,888 women aged 15–49 years who were married or cohabiting and had a recent birth at the time of data collection. Outcome variables were the number of antenatal care (ANC) visits and the place of delivery for the most recent birth. Explanatory variables included sociodemographic characteristics, digital exposure indicators, and knowledge-based empowerment measures. Multivariable logistic regression models were used to estimate adjusted associations. Results ANC utilisation was significantly associated with age, education, wealth, residence, region, religion, household size, employment, occupation, and partner’s characteristics. Digital exposure variables (mobile phone ownership, internet use, frequency of use, and mobile financial transactions) were positively associated with ANC visits. Knowledge-based empowerment factors, including exposure to family planning information via SMS and social media, and key health knowledge indicators, were also significant predictors. Similar patterns were observed for place of delivery, with most sociodemographic, digital exposure, and knowledge-based empowerment variables significantly influencing the likelihood of facility-based delivery. Conclusions Digital exposure and knowledge-based empowerment complement traditional socioeconomic determinants of maternal health behaviour. Policies that expand digital access and strengthen women’s health knowledge, alongside efforts to reduce structural inequalities, are essential for improving maternal health service utilisation and accelerating progress toward SDGs 3 and 5 in Nigeria. Maternal health behaviour Antenatal care utilisation Facility-based delivery Digital health Mobile health Health literacy Knowledge-based empowerment Nigeria NDHS Low- and middle-income countries Background Health behaviour plays a critical role in determining health outcomes, particularly in maternal and reproductive health contexts. In many low- and middle-income countries (LMICs), health behaviours such as antenatal care (ANC) utilisation, facility-based delivery, immunisation uptake, and family planning adoption are essential determinants of maternal and child survival [ 1 , 2 ]. Nigeria continues to face significant maternal and child health challenges despite improvements in healthcare infrastructure and policy interventions. Maternal mortality remains among the highest globally, with preventable complications during pregnancy and childbirth contributing significantly to poor health outcomes [ 3 ]. Addressing these challenges requires effective behaviour change interventions that encourage women to adopt positive health-seeking practices. In recent years, digital technologies have emerged as powerful tools for influencing health behaviour and improving access to healthcare information. Information and Communication Technologies (ICTs), including mobile phones, internet platforms, and social media networks, have significantly expanded opportunities for disseminating health information and promoting behavioural change [ 4 , 5 ]. These technologies have become particularly important in Nigeria, where geographic barriers, limited healthcare personnel, and infrastructural constraints often restrict access to traditional health education channels. Mobile health (mHealth) interventions have demonstrated significant potential for improving maternal and child health outcomes. Through mechanisms such as SMS reminders, mobile health applications, and digital health education platforms, mHealth initiatives enable the dissemination of timely and relevant health information to women of reproductive age [ 6 , 7 ]. Empirical studies indicate that women who receive digital health messages are more likely to attend antenatal care appointments, adopt safer childbirth practices, and comply with immunisation schedules [ 8 ]. Consequently, digital technologies have become important instruments for behaviour change communication in maternal health programs. Digital exposure, defined as access to and engagement with digital technologies such as mobile phones, the internet, and online communication platforms, plays a critical role in shaping health behaviour. Through digital exposure, women can access information related to pregnancy care, family planning, nutrition, and child health, enabling them to make informed decisions about their well-being [ 9 , 10 ]. Exposure to digital health information also strengthens women’s awareness of health risks and available healthcare services, which can motivate preventive health actions. Despite the rapid expansion of digital technologies in Nigeria, significant inequalities remain in access and utilisation. Nigeria has one of the largest telecommunications markets in Africa, with mobile phone penetration exceeding 103 percent of the population by 2023 [ 11 ]. However, gender disparities in digital access remain pronounced. The GSMA Mobile Gender Gap Report indicates that only about two-thirds of Nigerian women own mobile phones compared to more than four-fifths of men [ 12 ]. Furthermore, women are significantly less likely to access mobile internet services due to affordability constraints, limited digital literacy, and socio-cultural restrictions. These disparities highlight the importance of considering sociodemographic characteristics when designing behaviour change interventions. Factors such as age, education, wealth status, residence, marital dynamics, and household characteristics influence women’s ability to access digital technologies and utilise them effectively for health purposes [ 13 , 14 ]. Married women in rural or economically disadvantaged communities often face additional barriers including limited financial autonomy, restricted decision-making power, and lower levels of formal education [ 15 ]. Beyond access to digital technologies, women’s ability to translate health information into meaningful behavioural change depends on their level of empowerment. Knowledge-based empowerment refers to the acquisition of knowledge, confidence, and decision-making capacity that enables individuals to act on health information and adopt healthy practices [ 16 , 17 ] Digital exposure can enhance such empowerment by providing women with accessible health information and strengthening their ability to participate in healthcare decisions. Understanding the relationships among sociodemographic characteristics, digital exposure, knowledge-based empowerment, and health behaviour is therefore critical for designing effective behaviour change interventions. Furthermore, harnessing these relationships has important implications for achieving global development objectives. Digital health interventions contribute to Sustainable Development Goal (SDG) 3, which aims to ensure healthy lives and promote well-being for all, particularly by reducing maternal and child mortality [ 18 ]. At the same time, expanding women’s digital access and empowerment supports SDG 5, which seeks to achieve gender equality and empower all women and girls [ 19 ]. Despite increasing attention to digital health in Nigeria, limited studies have examined how sociodemographic factors, digital exposure, and knowledge-based empowerment interact to influence health behaviour among married women. Thus, this study investigates how these three domains interact to predict health behaviour among married women in Nigeria and how these relationships can inform health behaviour change interventions necessary to attain SDGs 3 and 5 in Nigeria by 2030 [ 20 ]. Theoretical Framework The Technology Acceptance Model (TAM) provides a framework for understanding individuals’ adoption of digital technologies [ 21 ]. According to TAM, technology adoption is influenced primarily by two perceptions: perceived usefulness and perceived ease of use. In the context of digital health interventions, perceived usefulness refers to women’s belief that digital platforms can improve their access to health information and healthcare services. Perceived ease of use refers to the extent to which women perceive digital technologies as simple and accessible [ 22 ]. When women perceive digital tools as useful and easy to use, they are more likely to adopt them for health-related purposes. The Health Belief Model (HBM) explains how individuals’ perceptions influence their health behaviour [ 23 ]. The model suggests that behaviour is influenced by perceived susceptibility to illness, perceived severity of health risks, perceived benefits of preventive actions, and perceived barriers to health behaviour. Digital platforms can influence these perceptions by providing timely health information that highlights maternal health risks and promotes preventive care practices [ 24 ]. The Social Determinants of Health (SDOH) framework emphasises that health outcomes are shaped by social and economic conditions such as income, education, employment, and living environment [ 25 ]. These factors influence individuals’ ability to access healthcare services and adopt healthy behaviours. In Nigeria, these determinants intersect with digital access. Women with higher education and income levels are more likely to access digital health information and utilise digital health services [ 26 ]. Intersectionality theory highlights how overlapping social identities such as gender, socioeconomic status, and geographic location interact to create multiple layers of disadvantage [ 27 ]. In Nigeria, married women often face intersecting barriers including gender norms, limited financial autonomy, and restricted digital access [ 15 ]. Applying an intersectional perspective helps explain disparities in digital health adoption and highlights the need for inclusive behaviour change interventions that address these structural inequalities. Review of Empirical Literature Sociodemographic characteristics significantly influence digital access and utilisation. Education, income, age, and geographic location are among the most important predictors of mobile phone ownership and internet use [ 13 , 14 ]. Women with higher educational attainment and stable income sources are more likely to own smartphones and engage with digital platforms. Urban residents also tend to have greater digital access due to better telecommunications infrastructure [ 28 ]. Gender disparities in digital access remain a major challenge across many African countries. Research indicates that women are consistently less likely than men to own mobile phones and access digital services [ 12 , 29 ]. These inequalities reduce women’s exposure to digital health information and limit the potential impact of digital health interventions. Digital technologies have become important tools for health promotion and behaviour change communication. Mobile phones, internet platforms, and social media networks enable health authorities to disseminate health messages rapidly and at relatively low cost [ 2 , 5 ]. In Nigeria, studies show that SMS reminders and mobile applications significantly improve antenatal care attendance and adherence to maternal health guidelines [ 6 , 7 ]. Studies show that mobile messaging interventions significantly increased facility-based deliveries among women in Cross River State [ 8 ]. However, the effectiveness of digital behaviour change interventions depends largely on users’ level of digital exposure and their ability to access and interpret digital health information. Digital technologies can enhance women’s empowerment by providing access to health information that strengthens their knowledge and decision-making capacity. Knowledge-based empowerment occurs when individuals gain sufficient information and confidence to make informed choices regarding their health and well-being [ 16 ]. Studies show that exposure to digital health information increases women’s awareness of maternal health risks and improves their understanding of preventive healthcare practices [ 30 ]. Mobile health programs that provide health education messages have been shown to improve maternal health literacy and encourage proactive health behaviours. However, digital empowerment depends not only on access to devices but also on digital literacy and social support. Women who lack digital skills may not fully benefit from digital health interventions even if they own mobile phones [ 31 ]. Knowledge-based empowerment plays a crucial role in shaping health behaviour. Women who possess adequate health knowledge are more likely to seek preventive healthcare services and adopt recommended health practices [ 17 ]. Empirical studies show that maternal health education programs significantly improve antenatal care attendance, skilled birth attendance, and child health practices [ 32 ]. By strengthening women’s knowledge and decision-making capacity, empowerment initiatives can promote sustained behavioural change, although empowerment outcomes are also influenced by broader social and cultural factors such as gender norms, household decision-making dynamics, and access to financial resources [ 15 ]. Methods Data Source and Study Population This study utilises data from the 2018 Nigeria Demographic and Health Survey (NDHS), conducted by the National Population Commission (NPC) in collaboration with ICF International under the Demographic and Health Surveys Program. The NDHS 2018 provides nationally representative data on population, maternal and child health, fertility, and related demographic and socioeconomic indicators. The survey covered all 36 states of Nigeria and the Federal Capital Territory (FCT), encompassing 774 Local Government Areas (LGAs) and 74 strata defined by urban–rural residence and geopolitical zones. A stratified two-stage cluster sampling design was employed using enumeration areas (EAs) from the 2006 census as the sampling frame. In the first stage, 1,400 EAs were selected with probability proportional to size (PPS). In the second stage, 30 households were systematically selected from each EA, resulting in a total of 42,000 households initially targeted. Data collection was completed in 1,389 clusters, yielding 40,427 successfully interviewed households, with a household response rate of 99%. Within these households, 41,821 women aged 15–49 years were successfully interviewed, achieving a response rate of approximately 99%. Of the 41,821 women interviewed, 28,888 (69 per cent) reported being married or living with a partner and are the focus of this study. Study Variables The analysis focuses on women of reproductive age (15–49 years) who had a live birth within the five years preceding the survey. The primary outcome variables are maternal health behaviours operationalized as: (i) use of antenatal care (ANC) services, measured as the number of visits during pregnancy (coded as six or more visits versus five or fewer or none, consistent with global monitoring indicators); and (ii) place of delivery, captured as a dichotomous variable indicating health facility delivery versus home or non-facility delivery [ 33 , 34 ]. Explanatory variables are grouped into three domains. Sociodemographic characteristics include age, education, household wealth index, place of residence, region, marital status, religion, household size, employment status, and partner’s education [ 35 – 37 ]. Digital exposure indicators include mobile phone ownership, internet use, frequency of internet use, use of mobile phone for financial transactions, and exposure to family planning information via SMS or social media [ 38 ]. Health knowledge variables include awareness of maternal and child health services and knowledge of key health practices (knowledge of the ovulatory cycle, belief that preventive medicine keeps the mother and baby healthy, and belief that malaria can be fully cured by medicine), which are important drivers of health service uptake in LMIC contexts [ 39 , 40 ]. Statistical Analysis All analyses applied the NDHS sampling weights to account for the complex survey design and ensure nationally representative estimates. Descriptive statistics summarise respondents’ characteristics and the distribution of key variables. Bivariate analyses using chi-square tests assess associations between explanatory variables and maternal health outcomes. Binary logistic regression models were subsequently fitted to examine the independent associations of sociodemographic factors, digital exposure, and health knowledge with ANC utilization and facility delivery, controlling for potential confounders. Clustering at the EA level and stratification by region and urban–rural residence are explicitly accounted for in the regression models. Data management and statistical analysis were conducted using IBM SPSS Statistics (Version 27). The thresholds for statistical significance were set at p < 0.001, p < 0.01, and p < 0.05. Model Specification This study specifies two empirical models with health behaviour as the dependent variable and sociodemographic, digital exposure, and health knowledge factors as independent variables: Model 1 (Use of ANC): UseANC = β₀ + β₁Age + β₂Edu + β₃WIndex + β₄Resid + β₅Reg + β₆MSt + β₇Relig + β₈NHHMem + β₉EmpStat + β₁₀OccupCat + β₁₁HPAge + β₁₂HPEduc + β₁₃MPOwn + β₁₄UseInter + β₁₅FreInterUse + β₁₆UseMPFin + β₁₇HFPSMS + β₁₈HFPSocMed + β₁₉HORSol + β₂₀KnoOvuC + β₂₁PreMedKMH + β₂₂PreMedKBH + β₂₃MalFulCMed + ε Model 2 (Place of Delivery): PlacDel = α₀ + α₁Age + α₂Edu + α₃WIndex + α₄Resid + α₅Reg + α₆MSt + α₇Relig + α₈NHHMem + α₉EmpStat + α₁₀OccupCat + α₁₁HPAge + α₂HPEduc + α₁₃MPOwn + α₁₄UseInter + α₁₅FreInterUse + α₁₆UseMPFin + α₁₇HFPSMS + α₁₈HFPSocMed + α₁₉HORSol + α₂₀KnoOvuC + α₂₁PreMedKMH + α₂₂PreMedKBH + α₂₃MalFulCMed + µ Where UseANC = use of antenatal care services (6 + visits); PlacDel = place of delivery (health facility); SocDemo = sociodemographic factors (Age, Edu, WIndex, Resid, Reg, MSt, Relig, NHHMem, EmpStat, OccupCat, HPAge, HPEduc); DigExp = digital exposure (MPOwn, UseInter, FreInterUse, UseMPFin); HeaInfo = health information access (HFPSMS, HFPSocMed, HORSol); HeaKno = health knowledge (KnoOvuC, PreMedKMH, PreMedKBH, MalFulCMed); ε, µ = error terms. Results Sample Description Table 1 Sample frequency distribution of study participants by sociodemographic, digital exposure, health information, and health knowledge variables (2018 NDHS; n = 28,888) Variable Freq Valid % Variable Freq Valid % Sociodemographic Characteristics Internet Use Frequency Age of the Woman Not at all 26,365 91.3 29 or younger 12,112 41.9 At least once or more a week 2,523 8.7 30 to 39 10,092 34.9 Use of Mobile Phone for Financial Transactions 40 or older 6,684 23.2 No or missing 25,306 87.6 Highest Level of Education Yes No education/Primary 17,535 60.7 Yes 3,582 12.4 Wealth Index – Quintile Health Information Access Measures Poorest 6,395 22.1 Heard of Family Planning via SMS: No 28,023 97.0 Poorer 6,267 21.7 Heard of Family Planning via SMS: Yes 865 3.0 Middle 5,853 20.3 Heard of Family Planning via Social Media: No 27,901 96.6 Richer 5,531 19.1 Heard of Family Planning via Social Media: Yes 987 3.4 Richest 4,842 16.8 Heard of ORS: Never heard of ORS 4,161 14.4 Residence Heard of or used ORS Rural 18,485 64.0 Heard of or used ORS 24,727 85.6 Geopolitical Region Health Knowledge North Central 5,268 18.2 Knowledge of Ovulatory Cycle: Don’t know 887 3.1 North East 5,668 19.6 Wrong answers 12,135 42.0 North West 8,115 28.1 Correct answer 15,866 54.9 South East 3,207 11.1 Preventive Medicine Keeps Mother Healthy: Agree 27,454 95.0 South South 2,962 10.3 Preventive Medicine Keeps Baby Healthy: Agree 27,453 95.0 South West 3,668 12.7 Malaria Can Be Fully Cured by Medicine: Agree 25,766 89.2 Religion Use of Antenatal Care Services Traditionalist and Other 230 0.8 Don’t know/Other 13,548 46.9 Islam 16,396 56.8 5 or less visits 8,315 28.8 Catholic 2,633 9.1 6 or more visits 7,025 24.3 Other Christian 9,629 33.3 Place of Delivery Number of Household Members 7 or more 11,391 39.4 Home/Other/Missing 20,573 71.2 5 to 6 7,870 27.2 Private facility 2,502 8.7 4 or less 9,627 33.3 Public facility 5,813 20.1 Employment and Partner Characteristics Digital Exposure Indicators Employed 20,264 70.1 Mobile Phone Ownership: No 13,674 47.3 Professional/Technical/Managerial 1,677 5.8 Mobile Phone Ownership: Yes 15,214 52.7 Partner aged 40 or older 16,633 57.6 Internet Use: Never used 25,932 89.8 Partner: No educ./Primary 14,632 50.7 Internet Use: Used (last 12 months+) 2,956 10.2 Table 1 presents the percentage distribution of the study participants by sociodemographic characteristics, digital exposure, and health information access. The majority of the women were aged 29 or younger (41.9%), had no education or only primary-level education (60.7%), and belonged to the middle/poorer/poorest wealth quintiles (64.1%). The majority resided in rural areas (64.0%), were from the northern region (65.9%), and were Muslim (56.8%). Of the 52.7% of women who owned a mobile phone, only 10.2% had used the internet in the last 12 months, while just 12.4% used a mobile phone for financial transactions. Only 3.0% heard of family planning via SMS and 3.4% via social media. Over half (54%) reported correct knowledge of the ovulatory cycle, and overwhelming majorities agreed that preventive medicine keeps the mother healthy (95%) and the baby healthy (95%). Most respondents (89.2%) agreed that malaria can be fully cured by medicine. A small proportion (24.3%) used ANC services at least six times in their last pregnancy, and 28.8% reported using a health facility for their last childbirth. Bivariate Results Table 2 Chi-square association between health behaviour outcomes and sociodemographic, digital exposure, and health knowledge factors Variable / Subcategory ANC < 6 visits or none (%) ANC 6 + visits (%) Non-facility delivery (%) Health facility delivery (%) p-value Age of Woman (p = 0.000) 29 or younger 41.2 44.3 40.4 45.7 30 to 39 32.0 44.1 31.6 43.3 40 or older 26.8 11.7 28.1 11.0 0.000 Education (p = 0.000) No education/Primary 69.1 34.5 72.0 32.7 Secondary 24.6 48.2 22.6 49.4 Higher 6.3 17.4 5.4 17.9 0.000 Wealth Index (p = 0.000) Poorest 26.6 8.4 28.3 6.9 Poorer 24.2 13.9 25.0 13.5 Middle 20.0 21.1 19.5 22.2 Richer 16.7 26.7 15.7 27.6 Richest 12.5 30.0 11.5 29.7 0.000 Residence (p = 0.000) Rural 69.3 47.5 70.8 47.1 Urban 30.7 52.5 29.2 52.9 0.000 Region (p = 0.000) North Central 18.3 18.0 16.5 22.5 North East 22.9 9.3 22.3 12.9 North West 33.0 12.8 34.8 11.4 South East 8.5 19.1 7.3 20.5 South South 9.0 14.3 10.1 10.7 South West 8.3 26.5 8.9 22.0 0.000 Religion (p = 0.000) Islam/Traditionalist/Other 63.9 37.7 65.2 38.7 All Christians 36.1 62.3 34.8 61.3 0.000 Household Size (p = 0.000) 7 or more 42.3 30.4 42.6 31.5 5 to 6 25.4 33.0 25.1 32.5 4 or less 32.3 36.5 32.2 36.0 0.000 Employment Status (p = 0.000) Unemployed 32.6 21.5 32.6 23.2 Employed 67.4 78.5 67.4 76.8 0.000 Partner Education (p = 0.000) No education/Primary 57.9 28.1 60.6 26.1 Secondary 29.3 47.7 28.1 48.1 Higher 12.8 24.1 11.4 25.9 0.000 Table 2 presents statistical associations between health behaviour outcomes and sociodemographic characteristics. Use of ANC services (six or more visits) and health facility delivery were significantly associated with all sociodemographic variables examined, including age, education, wealth index, place of residence, geopolitical region, religion, number of household members, employment status, occupation category, partner’s age, and partner’s education (all p-values = 0.000). Multivariate Results Table 3 Adjusted odds ratios (OR) and 95% confidence intervals (CI) for the associations between sociodemographic, digital exposure, and health knowledge factors and maternal health behaviour outcomes (ANC utilisation and place of delivery) Variable Model 1: ANC (6 + visits) OR (95% CI) p Model 2: Place of Delivery (Health Facility) OR (95% CI) p Sociodemographic Characteristics Age of the Woman 29 or younger (ref) 1.00 1.00 30 to 39 0.92 (0.85, 1.00) .047 0.91 (0.84, 0.98) .018 40 or older 0.29 (0.26, 0.32) .000 0.25 (0.23, 0.28) .000 Education No education/Primary (ref) 1.00 1.00 Secondary 1.34 (1.23, 1.45) .000 1.65 (1.53, 1.79) .000 Higher 1.78 (1.54, 2.05) .000 2.29 (1.99, 2.64) .000 Wealth Index Poorest (ref) 1.00 1.00 Poorer 1.31 (1.17, 1.47) .000 1.65 (1.47, 1.85) .000 Middle 1.49 (1.32, 1.68) .000 2.27 (2.02, 2.56) .000 Richer 1.52 (1.33, 1.73) .000 2.42 (2.13, 2.75) .000 Richest 1.66 (1.44, 1.92) .000 2.67 (2.31, 3.08) .000 Residence Rural (ref) 1.00 1.00 Urban 1.15 (1.07, 1.24) .000 1.18 (1.10, 1.27) .000 Geopolitical Region North Central (ref) 1.00 1.00 North East 0.53 (0.47, 0.59) .000 0.64 (0.57, 0.70) .000 North West 0.52 (0.47, 0.58) .000 0.35 (0.32, 0.39) .000 South East 1.91 (1.70, 2.14) .000 1.63 (1.45, 1.82) .000 South South 1.46 (1.30, 1.63) .000 0.54 (0.49, 0.61) .000 South West 2.75 (2.48, 3.05) .000 1.31 (1.18, 1.45) .000 Religion Islam/Traditionalist/Other (ref) 1.00 1.00 All Christians 1.10 (1.01, 1.20) .038 1.14 (1.05, 1.24) .020 Number of Household Members 7 or more (ref) 1.00 1.00 5 to 6 0.94 (0.87, 1.02) .115 0.91 (0.84, 0.98) .013 4 or less 0.67 (0.61, 0.72) .000 0.62 (0.57, 0.67) .000 Employment Status Unemployed (ref) 1.00 1.00 Employed 1.13 (1.04, 1.23) .006 1.12 (1.03, 1.21) .009 Occupation Category Manual/Agricultural/Other (ref) 1.00 1.00 Clerical/Sales/Services 1.02 (0.95, 1.10) .553 0.95 (0.88, 1.02) .180 Professional/Technical/Managerial 0.98 (0.86, 1.12) .800 0.83 (0.72, 0.95) .006 Partner’s Age 29 or younger (ref) 1.00 1.00 30 to 39 1.07 (0.96, 1.19) .242 1.11 (1.00, 1.24) .059 40 or older 0.84 (0.74, 0.96) .008 0.83 (0.73, 0.94) .003 Partner’s Education No education/Primary (ref) 1.00 1.00 Secondary 1.34 (1.24, 1.46) .000 1.54 (1.42, 1.67) .000 Higher 1.50 (1.34, 1.67) .000 1.84 (1.65, 2.04) .000 Digital Exposure Factors Mobile Phone Ownership No (ref) 1.00 1.00 Yes 1.30 (1.20, 1.40) .000 1.34 (1.24, 1.45) .000 Internet Use Never used (ref) 1.00 1.00 Used (last 12 months or earlier) 1.33 (1.08, 1.64) .009 1.06 (0.85, 1.31) .605 Internet Use Frequency Not at all (ref) 1.00 1.00 At least once or more a week 0.68 (0.55, 0.85) .001 0.99 (0.79, 1.24) .940 Use of Mobile Phone for Financial Transactions No or missing (ref) 1.00 1.00 Yes 0.90 (0.81, 1.00) .043 0.82 (0.74, 0.91) .000 Health Information Access Heard of Family Planning via SMS No (ref) 1.00 1.00 Yes 0.84 (0.70, 1.00) .048 0.84 (0.70, 1.00) .052 Heard of Family Planning via Social Media No (ref) 1.00 1.00 Yes 1.26 (1.07, 1.50) .007 1.24 (1.04, 1.47) .017 Heard of Oral Rehydration Solution Never heard of ORS (ref) 1.00 1.00 Heard of or used ORS 1.62 (1.47, 1.79) .000 1.46 (1.33, 1.60) .000 Health Knowledge Knowledge of the Ovulatory Cycle Else (ref) 1.00 1.00 Correct answer 1.11 (1.05, 1.19) .001 1.16 (1.09, 1.23) .000 Preventive Medicine Keeps Mother Healthy Disagree or other (ref) 1.00 1.00 Agree 1.35 (1.08, 1.67) .007 1.65 (1.34, 2.03) .000 Preventive Medicine Keeps Baby Healthy Disagree or other (ref) 1.00 1.00 Agree 1.16 (0.94, 1.44) .175 1.10 (0.90, 1.35) .353 Malaria Can Be Fully Cured by Medicine Disagree/Don’t know (ref) 1.00 1.00 Agree 1.20 (1.08, 1.33) .001 1.01 (0.91, 1.11) .876 Note: Significance levels p ≤ .1, p ≤ .05, p ≤ .01, p ≤ .001. ANC model: Chi-square = 5764.423; -2 Log likelihood = 26284.990; Nagelkerke R² = 0.270. POD model: Chi-square = 7671.022; -2 Log likelihood = 27006.277; Nagelkerke R² = 0.334. ANC = antenatal care; CI = confidence interval; NDHS = Nigeria Demographic and Health Survey; OR = odds ratio; ORS = oral rehydration solution; POD = place of delivery; ref = reference category. Table 3 presents two models showing adjusted odds ratios (OR) and 95% confidence intervals (CI) for associations between maternal health behaviour and their predictors. Model 1 examines ANC utilisation (six or more visits) and explains 27.0% of the variation (Nagelkerke R² = 0.270). Model 2 examines health facility delivery and explains 33.4% of the variation (Nagelkerke R² = 0.334). With respect to ANC utilisation, odds were lower for older women (age 30–39: OR = 0.92, CI = 0.85–1.00; age 40+: OR = 0.29, CI = 0.26–0.32) and higher for women with secondary (OR = 1.34, CI = 1.23–1.45) or higher education (OR = 1.78, CI = 1.54–2.05). Wealthier women were more likely to use ANC services, with odds increasing progressively from poorer (OR = 1.31) to richest quintile (OR = 1.66). Urban residence (OR = 1.15), Christian religion (OR = 1.10), and employment (OR = 1.13) were positively associated with ANC utilisation. Regional variations were pronounced, with higher odds in the South West (OR = 2.75) and South East (OR = 1.91), and lower odds in North East (OR = 0.53) and North West (OR = 0.52) relative to North Central. Mobile phone ownership (OR = 1.30) and internet use (OR = 1.33) were positively associated with ANC visits. Women who had heard of family planning via social media (OR = 1.26) and had heard of or used oral rehydration solution (OR = 1.62) also had higher odds of adequate ANC. Knowledge of the ovulatory cycle (OR = 1.11), belief that preventive medicine keeps the mother healthy (OR = 1.35), and belief that malaria can be cured (OR = 1.20) were independently associated with higher ANC utilisation. Similar patterns were observed for health facility delivery. Education, wealth, urban residence, and partner’s education were all positively associated with facility delivery. Mobile phone ownership (OR = 1.34), exposure to family planning via social media (OR = 1.24), awareness of ORS (OR = 1.46), knowledge of the ovulatory cycle (OR = 1.16), and belief that preventive medicine keeps the mother healthy (OR = 1.65) were significantly associated with higher odds of facility delivery. Discussion The findings of this study demonstrate that digital exposure and knowledge-based empowerment are critical determinants of maternal health behaviour among married women in Nigeria. Specifically, access to health information and health knowledge significantly influence the utilisation of maternal health services such as antenatal care and health facility delivery. These findings reinforce the growing body of literature that emphasises the transformative role of digital technologies in promoting positive health behaviours in low- and middle-income countries [ 6 , 2 , 5 ]. The results further indicate that sociodemographic characteristics significantly shape maternal health behaviour in Nigeria. Variables such as age, education, wealth status, residence, geopolitical region, religion, household size, employment status, and partner’s educational level were found to significantly influence women’s use of antenatal care services. These findings align strongly with the Social Determinants of Health framework, which posits that health outcomes are influenced by structural conditions such as education, income, occupation, and geographic location [ 25 , 26 ]. Women with higher socioeconomic status, greater educational attainment, and urban residence generally possess greater access to health services and health information, thereby increasing their likelihood of engaging in preventive health behaviours. The strong influence of wealth, education, and geographic region observed in this study further highlights persistent inequalities in access to digital health resources across Nigeria. In contrast, women living in rural areas and economically disadvantaged communities, particularly in northern Nigeria, remain relatively marginalised. This pattern reflects the digital divide, which describes the unequal distribution of access to digital technologies across social groups [ 12 , 29 ]. The digital divide has important implications for maternal health outcomes because women without access to digital technologies are less likely to receive health information that motivates preventive health behaviours. This finding emphasises the need for targeted digital inclusion initiatives that specifically address the needs of rural, low-income, and less-educated women in Nigeria. The study findings demonstrate that digital exposure, including mobile phone ownership and internet use, is positively associated with both ANC utilisation and health facility delivery. Women who owned mobile phones had 1.30 times higher odds of adequate ANC utilisation and 1.34 times higher odds of facility delivery compared to those without phones. These findings are consistent with evidence from Nigeria and Sub-Saharan Africa showing that mobile phone ownership enhances women’s access to maternal health information and facilitates timely utilisation of healthcare services [ 13 , 4 ]. The positive association between mobile phone ownership and health facility delivery further suggests that mobile health (mHealth) interventions can be effective tools for promoting safe delivery practices. Women who were exposed to digital health information, such as messages about family planning through SMS or social media and information about ORS, were significantly more likely to utilise ANC services. This aligns with evidence from DHS analyses in Sub-Saharan Africa showing that women exposed to health information through media and communication channels are more likely to attend recommended antenatal care visits [ 41 – 43 ]. Furthermore, access to health information enhances maternal health literacy and strengthens women’s perception of the importance and benefits of preventive maternal healthcare services [ 44 – 46 ]. Knowledge-based empowerment indicators, such as knowledge of the ovulatory cycle, belief in the benefits of preventive medicine, and understanding that malaria can be cured, were independently associated with increased utilisation of ANC services [ 43 , 47 ]. Broader health literacy, including understanding the importance of preventive medicine and recognising treatable conditions such as malaria, has been shown to improve women’s perceptions of susceptibility to pregnancy-related complications and the benefits of skilled maternal care [ 48 , 49 ]. Evidence from maternal health studies in LMICs shows that skilled birth attendants and access to emergency obstetric services are among the most effective interventions for preventing maternal and neonatal deaths [ 34 , 50 ]. Evidence from Nigeria further highlights the importance of institutional delivery, as findings from NDHS studies show that a substantial proportion of births still occur outside health facilities [ 51 , 52 ]. Multilevel analyses of the 2018 NDHS reveal that health facility delivery is strongly associated with prior ANC attendance, maternal education, and household wealth [ 52 , 53 ]. Digital exposure and knowledge-based empowerment significantly increase women’s likelihood of delivering in health facilities, suggesting that digital health interventions can play an important role in promoting safe childbirth practices. From a policy perspective, these findings suggest that behaviour change interventions should focus on expanding digital inclusion among women. Programs that promote mobile phone ownership, increase internet access, and support digital literacy can significantly enhance women’s exposure to health information [ 54 – 56 ]. Furthermore, initiatives that encourage women’s use of mobile platforms for communication, financial transactions, and information seeking can strengthen their engagement with digital health services [ 11 , 57 ]. Expanding women’s digital access and literacy represents a critical pathway for enhancing health knowledge, promoting preventive health behaviours, and ultimately improving maternal health outcomes in developing countries [ 54 , 55 ]. Conclusions This study examined the associations between sociodemographic characteristics, digital exposure, knowledge-based empowerment, and maternal health behaviour among married women in Nigeria using the 2018 NDHS. The findings demonstrate that maternal health behaviour, particularly ANC utilisation and health facility delivery, is influenced by a complex interaction of structural, technological, and cognitive factors. Sociodemographic characteristics such as education, wealth status, residence, and partner’s educational level significantly shape women’s access to healthcare services through digital exposure. Digital exposure, measured through mobile phone ownership, internet use, social media engagement, and mobile financial transactions, plays an important role in promoting positive maternal health behaviours. In addition, knowledge-based empowerment emerged as a critical pathway through which digital exposure influences health behaviour. Women who possess accurate reproductive health knowledge and positive beliefs about preventive medicine are more likely to utilise ANC services and deliver in health facilities. These findings suggest that effective behaviour change interventions should adopt an integrated approach that combines digital health strategies with women’s empowerment initiatives. Expanding digital access, promoting digital literacy, and strengthening women’s access to reproductive health information can significantly improve maternal health outcomes. By harnessing the relationships between sociodemographic characteristics, digital exposure, and knowledge-based empowerment, policymakers and health practitioners can design more effective behaviour change interventions that improve maternal health and accelerate progress toward SDGs 3 and 5 in Nigeria by 2030 [ 18 , 20 ]. Abbreviations ANC Antenatal care AOR Adjusted odds ratio CI Confidence interval DHS Demographic and Health Survey EA Enumeration area FCT Federal Capital Territory HBM Health Belief Model ICT Information and Communication Technology LMIC Low- and middle-income country mHealth Mobile health NDHS Nigeria Demographic and Health Survey NPC National Population Commission OR Odds ratio ORS Oral rehydration solution POD Place of delivery PPS Probability proportional to size SDG Sustainable Development Goal SDOH Social Determinants of Health SMS Short message service SPSS Statistical Package for the Social Sciences TAM Technology Acceptance Model. Declarations Ethics approval and consent to participate This study is based on secondary analysis of publicly available, de-identified data from the 2018 Nigeria Demographic and Health Survey (NDHS), conducted by the National Population Commission (NPC) of Nigeria in collaboration with ICF International. Ethical approval for the NDHS was obtained by the survey administrators from the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. All participants provided written informed consent prior to participation in the original survey. As this study involves secondary analysis of anonymised data with no direct contact with human participants, additional ethics approval was not required. Data access was obtained through registration with the DHS Program ( www.dhsprogram.com ). Consent for publication Not applicable. This manuscript does not contain data from any individual person in any identifiable form. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of an MSc Dissertation in Demography and Social Statistics at Covenant University, Ota, Nigeria. Author Contribution M.O. conceptualised the study, supervised the research, contributed to the theoretical framework, and reviewed and revised the manuscript. A.E. conducted the literature review, performed the data analysis, drafted the original manuscript, and prepared the final version for submission. All authors read and approved the final manuscript. Acknowledgements The authors acknowledge the DHS Program for providing access to the 2018 NDHS dataset, and Covenant University, Ota, for institutional support. Authors’ information M.O. is a Senior Lecturer in the Department of Economic & Development Studies and a member of the Public-Private Partnership Research Cluster at Covenant University, Ota, Nigeria, with research interests in maternal and child health, population studies, and development economics. A.E. is a recent MSc graduate in Demography and Social Statistics from Covenant University, Ota, Nigeria, with research interests in digital health, maternal health behaviour, and health equity. Data Availability The datasets analysed during the current study are publicly available through the DHS Program repository. Data can be requested and downloaded at no cost following registration at: https://dhsprogram.com/data/available-datasets.cfm References UN-DESA. The Sustainable Development Goals Report 2023: Special Edition [Internet]. United Nations; 2023 [cited 2023 Dec 16]. (The Sustainable Development Goals Report). 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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-9246834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629811097,"identity":"c9e63787-3058-49a4-9210-bfbf9c1c6e3b","order_by":0,"name":"Muyiwa Oladosun","email":"","orcid":"","institution":"Covenant University","correspondingAuthor":false,"prefix":"","firstName":"Muyiwa","middleName":"","lastName":"Oladosun","suffix":""},{"id":629811099,"identity":"68dcc852-9d96-441e-8f58-83b0216dbac5","order_by":1,"name":"Anthony Etim","email":"data:image/png;base64,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","orcid":"","institution":"Covenant University","correspondingAuthor":true,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Etim","suffix":""}],"badges":[],"createdAt":"2026-03-27 16:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9246834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9246834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182950,"identity":"86b3ef37-5d74-4856-a92a-8a46e9f89852","added_by":"auto","created_at":"2026-04-30 08:59:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":782748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9246834/v1/49f99e3f-cba3-4799-b71b-83c042012059.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging Sociodemographic, Digital Exposure, and Knowledge-Based Empowerment Factors to Improve Maternal Health Behaviour Among Married Women in Nigeria: A Cross-Sectional Analysis of the 2018 NDHS","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth behaviour plays a critical role in determining health outcomes, particularly in maternal and reproductive health contexts. In many low- and middle-income countries (LMICs), health behaviours such as antenatal care (ANC) utilisation, facility-based delivery, immunisation uptake, and family planning adoption are essential determinants of maternal and child survival [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nigeria continues to face significant maternal and child health challenges despite improvements in healthcare infrastructure and policy interventions. Maternal mortality remains among the highest globally, with preventable complications during pregnancy and childbirth contributing significantly to poor health outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Addressing these challenges requires effective behaviour change interventions that encourage women to adopt positive health-seeking practices.\u003c/p\u003e \u003cp\u003eIn recent years, digital technologies have emerged as powerful tools for influencing health behaviour and improving access to healthcare information. Information and Communication Technologies (ICTs), including mobile phones, internet platforms, and social media networks, have significantly expanded opportunities for disseminating health information and promoting behavioural change [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These technologies have become particularly important in Nigeria, where geographic barriers, limited healthcare personnel, and infrastructural constraints often restrict access to traditional health education channels.\u003c/p\u003e \u003cp\u003eMobile health (mHealth) interventions have demonstrated significant potential for improving maternal and child health outcomes. Through mechanisms such as SMS reminders, mobile health applications, and digital health education platforms, mHealth initiatives enable the dissemination of timely and relevant health information to women of reproductive age [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Empirical studies indicate that women who receive digital health messages are more likely to attend antenatal care appointments, adopt safer childbirth practices, and comply with immunisation schedules [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, digital technologies have become important instruments for behaviour change communication in maternal health programs.\u003c/p\u003e \u003cp\u003eDigital exposure, defined as access to and engagement with digital technologies such as mobile phones, the internet, and online communication platforms, plays a critical role in shaping health behaviour. Through digital exposure, women can access information related to pregnancy care, family planning, nutrition, and child health, enabling them to make informed decisions about their well-being [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Exposure to digital health information also strengthens women\u0026rsquo;s awareness of health risks and available healthcare services, which can motivate preventive health actions.\u003c/p\u003e \u003cp\u003eDespite the rapid expansion of digital technologies in Nigeria, significant inequalities remain in access and utilisation. Nigeria has one of the largest telecommunications markets in Africa, with mobile phone penetration exceeding 103 percent of the population by 2023 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, gender disparities in digital access remain pronounced. The GSMA Mobile Gender Gap Report indicates that only about two-thirds of Nigerian women own mobile phones compared to more than four-fifths of men [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, women are significantly less likely to access mobile internet services due to affordability constraints, limited digital literacy, and socio-cultural restrictions. These disparities highlight the importance of considering sociodemographic characteristics when designing behaviour change interventions. Factors such as age, education, wealth status, residence, marital dynamics, and household characteristics influence women\u0026rsquo;s ability to access digital technologies and utilise them effectively for health purposes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Married women in rural or economically disadvantaged communities often face additional barriers including limited financial autonomy, restricted decision-making power, and lower levels of formal education [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond access to digital technologies, women\u0026rsquo;s ability to translate health information into meaningful behavioural change depends on their level of empowerment. Knowledge-based empowerment refers to the acquisition of knowledge, confidence, and decision-making capacity that enables individuals to act on health information and adopt healthy practices [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Digital exposure can enhance such empowerment by providing women with accessible health information and strengthening their ability to participate in healthcare decisions. Understanding the relationships among sociodemographic characteristics, digital exposure, knowledge-based empowerment, and health behaviour is therefore critical for designing effective behaviour change interventions.\u003c/p\u003e \u003cp\u003eFurthermore, harnessing these relationships has important implications for achieving global development objectives. Digital health interventions contribute to Sustainable Development Goal (SDG) 3, which aims to ensure healthy lives and promote well-being for all, particularly by reducing maternal and child mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. At the same time, expanding women\u0026rsquo;s digital access and empowerment supports SDG 5, which seeks to achieve gender equality and empower all women and girls [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite increasing attention to digital health in Nigeria, limited studies have examined how sociodemographic factors, digital exposure, and knowledge-based empowerment interact to influence health behaviour among married women. Thus, this study investigates how these three domains interact to predict health behaviour among married women in Nigeria and how these relationships can inform health behaviour change interventions necessary to attain SDGs 3 and 5 in Nigeria by 2030 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eThe Technology Acceptance Model (TAM) provides a framework for understanding individuals\u0026rsquo; adoption of digital technologies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to TAM, technology adoption is influenced primarily by two perceptions: perceived usefulness and perceived ease of use. In the context of digital health interventions, perceived usefulness refers to women\u0026rsquo;s belief that digital platforms can improve their access to health information and healthcare services. Perceived ease of use refers to the extent to which women perceive digital technologies as simple and accessible [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. When women perceive digital tools as useful and easy to use, they are more likely to adopt them for health-related purposes.\u003c/p\u003e \u003cp\u003eThe Health Belief Model (HBM) explains how individuals\u0026rsquo; perceptions influence their health behaviour [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The model suggests that behaviour is influenced by perceived susceptibility to illness, perceived severity of health risks, perceived benefits of preventive actions, and perceived barriers to health behaviour. Digital platforms can influence these perceptions by providing timely health information that highlights maternal health risks and promotes preventive care practices [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Social Determinants of Health (SDOH) framework emphasises that health outcomes are shaped by social and economic conditions such as income, education, employment, and living environment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These factors influence individuals\u0026rsquo; ability to access healthcare services and adopt healthy behaviours. In Nigeria, these determinants intersect with digital access. Women with higher education and income levels are more likely to access digital health information and utilise digital health services [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntersectionality theory highlights how overlapping social identities such as gender, socioeconomic status, and geographic location interact to create multiple layers of disadvantage [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In Nigeria, married women often face intersecting barriers including gender norms, limited financial autonomy, and restricted digital access [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Applying an intersectional perspective helps explain disparities in digital health adoption and highlights the need for inclusive behaviour change interventions that address these structural inequalities.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eReview of Empirical Literature\u003c/h2\u003e \u003cp\u003eSociodemographic characteristics significantly influence digital access and utilisation. Education, income, age, and geographic location are among the most important predictors of mobile phone ownership and internet use [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Women with higher educational attainment and stable income sources are more likely to own smartphones and engage with digital platforms. Urban residents also tend to have greater digital access due to better telecommunications infrastructure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Gender disparities in digital access remain a major challenge across many African countries. Research indicates that women are consistently less likely than men to own mobile phones and access digital services [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These inequalities reduce women\u0026rsquo;s exposure to digital health information and limit the potential impact of digital health interventions.\u003c/p\u003e \u003cp\u003eDigital technologies have become important tools for health promotion and behaviour change communication. Mobile phones, internet platforms, and social media networks enable health authorities to disseminate health messages rapidly and at relatively low cost [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Nigeria, studies show that SMS reminders and mobile applications significantly improve antenatal care attendance and adherence to maternal health guidelines [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies show that mobile messaging interventions significantly increased facility-based deliveries among women in Cross River State [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the effectiveness of digital behaviour change interventions depends largely on users\u0026rsquo; level of digital exposure and their ability to access and interpret digital health information.\u003c/p\u003e \u003cp\u003eDigital technologies can enhance women\u0026rsquo;s empowerment by providing access to health information that strengthens their knowledge and decision-making capacity. Knowledge-based empowerment occurs when individuals gain sufficient information and confidence to make informed choices regarding their health and well-being [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies show that exposure to digital health information increases women\u0026rsquo;s awareness of maternal health risks and improves their understanding of preventive healthcare practices [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Mobile health programs that provide health education messages have been shown to improve maternal health literacy and encourage proactive health behaviours. However, digital empowerment depends not only on access to devices but also on digital literacy and social support. Women who lack digital skills may not fully benefit from digital health interventions even if they own mobile phones [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKnowledge-based empowerment plays a crucial role in shaping health behaviour. Women who possess adequate health knowledge are more likely to seek preventive healthcare services and adopt recommended health practices [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Empirical studies show that maternal health education programs significantly improve antenatal care attendance, skilled birth attendance, and child health practices [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. By strengthening women\u0026rsquo;s knowledge and decision-making capacity, empowerment initiatives can promote sustained behavioural change, although empowerment outcomes are also influenced by broader social and cultural factors such as gender norms, household decision-making dynamics, and access to financial resources [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Study Population\u003c/h2\u003e \u003cp\u003eThis study utilises data from the 2018 Nigeria Demographic and Health Survey (NDHS), conducted by the National Population Commission (NPC) in collaboration with ICF International under the Demographic and Health Surveys Program. The NDHS 2018 provides nationally representative data on population, maternal and child health, fertility, and related demographic and socioeconomic indicators. The survey covered all 36 states of Nigeria and the Federal Capital Territory (FCT), encompassing 774 Local Government Areas (LGAs) and 74 strata defined by urban\u0026ndash;rural residence and geopolitical zones.\u003c/p\u003e \u003cp\u003eA stratified two-stage cluster sampling design was employed using enumeration areas (EAs) from the 2006 census as the sampling frame. In the first stage, 1,400 EAs were selected with probability proportional to size (PPS). In the second stage, 30 households were systematically selected from each EA, resulting in a total of 42,000 households initially targeted. Data collection was completed in 1,389 clusters, yielding 40,427 successfully interviewed households, with a household response rate of 99%. Within these households, 41,821 women aged 15\u0026ndash;49 years were successfully interviewed, achieving a response rate of approximately 99%. Of the 41,821 women interviewed, 28,888 (69 per cent) reported being married or living with a partner and are the focus of this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cp\u003eThe analysis focuses on women of reproductive age (15\u0026ndash;49 years) who had a live birth within the five years preceding the survey. The primary outcome variables are maternal health behaviours operationalized as: (i) use of antenatal care (ANC) services, measured as the number of visits during pregnancy (coded as six or more visits versus five or fewer or none, consistent with global monitoring indicators); and (ii) place of delivery, captured as a dichotomous variable indicating health facility delivery versus home or non-facility delivery [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExplanatory variables are grouped into three domains. Sociodemographic characteristics include age, education, household wealth index, place of residence, region, marital status, religion, household size, employment status, and partner\u0026rsquo;s education [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Digital exposure indicators include mobile phone ownership, internet use, frequency of internet use, use of mobile phone for financial transactions, and exposure to family planning information via SMS or social media [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Health knowledge variables include awareness of maternal and child health services and knowledge of key health practices (knowledge of the ovulatory cycle, belief that preventive medicine keeps the mother and baby healthy, and belief that malaria can be fully cured by medicine), which are important drivers of health service uptake in LMIC contexts [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses applied the NDHS sampling weights to account for the complex survey design and ensure nationally representative estimates. Descriptive statistics summarise respondents\u0026rsquo; characteristics and the distribution of key variables. Bivariate analyses using chi-square tests assess associations between explanatory variables and maternal health outcomes. Binary logistic regression models were subsequently fitted to examine the independent associations of sociodemographic factors, digital exposure, and health knowledge with ANC utilization and facility delivery, controlling for potential confounders. Clustering at the EA level and stratification by region and urban\u0026ndash;rural residence are explicitly accounted for in the regression models.\u003c/p\u003e \u003cp\u003eData management and statistical analysis were conducted using IBM SPSS Statistics (Version 27). The thresholds for statistical significance were set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel Specification\u003c/h2\u003e \u003cp\u003eThis study specifies two empirical models with health behaviour as the dependent variable and sociodemographic, digital exposure, and health knowledge factors as independent variables:\u003c/p\u003e \u003cp\u003e \u003cem\u003eModel 1 (Use of ANC): UseANC\u0026thinsp;=\u0026thinsp;β₀ + β₁Age\u0026thinsp;+\u0026thinsp;β₂Edu\u0026thinsp;+\u0026thinsp;β₃WIndex\u0026thinsp;+\u0026thinsp;β₄Resid\u0026thinsp;+\u0026thinsp;β₅Reg\u0026thinsp;+\u0026thinsp;β₆MSt\u0026thinsp;+\u0026thinsp;β₇Relig\u0026thinsp;+\u0026thinsp;β₈NHHMem\u0026thinsp;+\u0026thinsp;β₉EmpStat\u0026thinsp;+\u0026thinsp;β₁₀OccupCat\u0026thinsp;+\u0026thinsp;β₁₁HPAge\u0026thinsp;+\u0026thinsp;β₁₂HPEduc\u0026thinsp;+\u0026thinsp;β₁₃MPOwn\u0026thinsp;+\u0026thinsp;β₁₄UseInter\u0026thinsp;+\u0026thinsp;β₁₅FreInterUse\u0026thinsp;+\u0026thinsp;β₁₆UseMPFin\u0026thinsp;+\u0026thinsp;β₁₇HFPSMS\u0026thinsp;+\u0026thinsp;β₁₈HFPSocMed\u0026thinsp;+\u0026thinsp;β₁₉HORSol\u0026thinsp;+\u0026thinsp;β₂₀KnoOvuC\u0026thinsp;+\u0026thinsp;β₂₁PreMedKMH\u0026thinsp;+\u0026thinsp;β₂₂PreMedKBH\u0026thinsp;+\u0026thinsp;β₂₃MalFulCMed\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eModel 2 (Place of Delivery): PlacDel\u0026thinsp;=\u0026thinsp;α₀ + α₁Age\u0026thinsp;+\u0026thinsp;α₂Edu\u0026thinsp;+\u0026thinsp;α₃WIndex\u0026thinsp;+\u0026thinsp;α₄Resid\u0026thinsp;+\u0026thinsp;α₅Reg\u0026thinsp;+\u0026thinsp;α₆MSt\u0026thinsp;+\u0026thinsp;α₇Relig\u0026thinsp;+\u0026thinsp;α₈NHHMem\u0026thinsp;+\u0026thinsp;α₉EmpStat\u0026thinsp;+\u0026thinsp;α₁₀OccupCat\u0026thinsp;+\u0026thinsp;α₁₁HPAge\u0026thinsp;+\u0026thinsp;α₂HPEduc\u0026thinsp;+\u0026thinsp;α₁₃MPOwn\u0026thinsp;+\u0026thinsp;α₁₄UseInter\u0026thinsp;+\u0026thinsp;α₁₅FreInterUse\u0026thinsp;+\u0026thinsp;α₁₆UseMPFin\u0026thinsp;+\u0026thinsp;α₁₇HFPSMS\u0026thinsp;+\u0026thinsp;α₁₈HFPSocMed\u0026thinsp;+\u0026thinsp;α₁₉HORSol\u0026thinsp;+\u0026thinsp;α₂₀KnoOvuC\u0026thinsp;+\u0026thinsp;α₂₁PreMedKMH\u0026thinsp;+\u0026thinsp;α₂₂PreMedKBH\u0026thinsp;+\u0026thinsp;α₂₃MalFulCMed\u0026thinsp;+\u0026thinsp;\u0026micro;\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere UseANC\u0026thinsp;=\u0026thinsp;use of antenatal care services (6\u0026thinsp;+\u0026thinsp;visits); PlacDel\u0026thinsp;=\u0026thinsp;place of delivery (health facility); SocDemo\u0026thinsp;=\u0026thinsp;sociodemographic factors (Age, Edu, WIndex, Resid, Reg, MSt, Relig, NHHMem, EmpStat, OccupCat, HPAge, HPEduc); DigExp\u0026thinsp;=\u0026thinsp;digital exposure (MPOwn, UseInter, FreInterUse, UseMPFin); HeaInfo\u0026thinsp;=\u0026thinsp;health information access (HFPSMS, HFPSocMed, HORSol); HeaKno\u0026thinsp;=\u0026thinsp;health knowledge (KnoOvuC, PreMedKMH, PreMedKBH, MalFulCMed); ε, \u0026micro;\u0026thinsp;=\u0026thinsp;error terms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSample Description\u003c/h2\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\u003eSample frequency distribution of study participants by sociodemographic, digital exposure, health information, and health knowledge variables (2018 NDHS; n\u0026thinsp;=\u0026thinsp;28,888)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValid %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSociodemographic Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eInternet Use Frequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of the Woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29 or younger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAt least once or more a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30 to 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of Mobile Phone for Financial Transactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40 or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo or missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest Level of Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education/Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index \u0026ndash; Quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHealth Information Access Measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeard of Family Planning via SMS: No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeard of Family Planning via SMS: Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeard of Family Planning via Social Media: No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeard of Family Planning via Social Media: Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeard of ORS: Never heard of ORS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHeard of or used ORS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeard of or used ORS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24,727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeopolitical Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHealth Knowledge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKnowledge of Ovulatory Cycle: Don\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWrong answers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrect answer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePreventive Medicine Keeps Mother Healthy: Agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePreventive Medicine Keeps Baby Healthy: Agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalaria Can Be Fully Cured by Medicine: Agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eUse of Antenatal Care Services\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditionalist and Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDon\u0026rsquo;t know/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIslam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 or less visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 or more visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Christian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlace of Delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Household Members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHome/Other/Missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 to 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrivate facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePublic facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment and Partner Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDigital Exposure Indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMobile Phone Ownership: No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/Technical/Managerial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMobile Phone Ownership: Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner aged 40 or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternet Use: Never used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner: No educ./Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternet Use: Used (last 12 months+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.2\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=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the percentage distribution of the study participants by sociodemographic characteristics, digital exposure, and health information access. The majority of the women were aged 29 or younger (41.9%), had no education or only primary-level education (60.7%), and belonged to the middle/poorer/poorest wealth quintiles (64.1%). The majority resided in rural areas (64.0%), were from the northern region (65.9%), and were Muslim (56.8%). Of the 52.7% of women who owned a mobile phone, only 10.2% had used the internet in the last 12 months, while just 12.4% used a mobile phone for financial transactions. Only 3.0% heard of family planning via SMS and 3.4% via social media. Over half (54%) reported correct knowledge of the ovulatory cycle, and overwhelming majorities agreed that preventive medicine keeps the mother healthy (95%) and the baby healthy (95%). Most respondents (89.2%) agreed that malaria can be fully cured by medicine. A small proportion (24.3%) used ANC services at least six times in their last pregnancy, and 28.8% reported using a health facility for their last childbirth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBivariate Results\u003c/h2\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\u003eChi-square association between health behaviour outcomes and sociodemographic, digital exposure, and health knowledge factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable / Subcategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANC\u0026thinsp;\u0026lt;\u0026thinsp;6 visits or none (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANC 6\u0026thinsp;+\u0026thinsp;visits (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-facility delivery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealth facility delivery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of Woman (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29 or younger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30 to 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40 or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education/Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Index (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIslam/Traditionalist/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Christians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 to 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner Education (p\u0026thinsp;=\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education/Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\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=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents statistical associations between health behaviour outcomes and sociodemographic characteristics. Use of ANC services (six or more visits) and health facility delivery were significantly associated with all sociodemographic variables examined, including age, education, wealth index, place of residence, geopolitical region, religion, number of household members, employment status, occupation category, partner\u0026rsquo;s age, and partner\u0026rsquo;s education (all p-values\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Results\u003c/h2\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\u003eAdjusted odds ratios (OR) and 95% confidence intervals (CI) for the associations between sociodemographic, digital exposure, and health knowledge factors and maternal health behaviour outcomes (ANC utilisation and place of delivery)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1: ANC (6\u0026thinsp;+\u0026thinsp;visits) OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2: Place of Delivery (Health Facility) OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic Characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of the Woman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29 or younger (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30 to 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.85, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.84, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40 or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29 (0.26, 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25 (0.23, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education/Primary (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34 (1.23, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.53, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.78 (1.54, 2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.29 (1.99, 2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.17, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.47, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.32, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.27 (2.02, 2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.33, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.42 (2.13, 2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.66 (1.44, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.67 (2.31, 3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.07, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18 (1.10, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeopolitical Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.47, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64 (0.57, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.47, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35 (0.32, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.91 (1.70, 2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.63 (1.45, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46 (1.30, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.49, 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.75 (2.48, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31 (1.18, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIslam/Traditionalist/Other (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Christians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.01, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.05, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Household Members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 or more (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 to 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.87, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.84, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.61, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.57, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.04, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.03, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual/Agricultural/Other (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClerical/Sales/Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.95, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.88, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/Technical/Managerial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.86, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.72, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner\u0026rsquo;s Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29 or younger (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30 to 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.96, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.00, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40 or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.74, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.73, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner\u0026rsquo;s Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education/Primary (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34 (1.24, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54 (1.42, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50 (1.34, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84 (1.65, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Exposure Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile Phone Ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.20, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.24, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever used (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed (last 12 months or earlier)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33 (1.08, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.85, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet Use Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least once or more a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.55, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.79, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of Mobile Phone for Financial Transactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo or missing (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.81, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.74, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Information Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeard of Family Planning via SMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.70, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.70, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeard of Family Planning via Social Media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.07, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.04, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeard of Oral Rehydration Solution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever heard of ORS (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeard of or used ORS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62 (1.47, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46 (1.33, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge of the Ovulatory Cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElse (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrect answer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.05, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.09, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive Medicine Keeps Mother Healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree or other (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.08, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.34, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreventive Medicine Keeps Baby Healthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree or other (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.94, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.90, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalaria Can Be Fully Cured by Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree/Don\u0026rsquo;t know (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20 (1.08, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.91, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: Significance levels p \u0026le; .1, p \u0026le; .05, p \u0026le; .01, p \u0026le; .001. ANC model: Chi-square\u0026thinsp;=\u0026thinsp;5764.423; -2 Log likelihood\u0026thinsp;=\u0026thinsp;26284.990; Nagelkerke R\u0026sup2; = 0.270. POD model: Chi-square\u0026thinsp;=\u0026thinsp;7671.022; -2 Log likelihood\u0026thinsp;=\u0026thinsp;27006.277; Nagelkerke R\u0026sup2; = 0.334. ANC\u0026thinsp;=\u0026thinsp;antenatal care; CI\u0026thinsp;=\u0026thinsp;confidence interval; NDHS\u0026thinsp;=\u0026thinsp;Nigeria Demographic and Health Survey; OR\u0026thinsp;=\u0026thinsp;odds ratio; ORS\u0026thinsp;=\u0026thinsp;oral rehydration solution; POD\u0026thinsp;=\u0026thinsp;place of delivery; ref\u0026thinsp;=\u0026thinsp;reference category.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents two models showing adjusted odds ratios (OR) and 95% confidence intervals (CI) for associations between maternal health behaviour and their predictors. Model 1 examines ANC utilisation (six or more visits) and explains 27.0% of the variation (Nagelkerke R\u0026sup2; = 0.270). Model 2 examines health facility delivery and explains 33.4% of the variation (Nagelkerke R\u0026sup2; = 0.334).\u003c/p\u003e \u003cp\u003eWith respect to ANC utilisation, odds were lower for older women (age 30\u0026ndash;39: OR\u0026thinsp;=\u0026thinsp;0.92, CI\u0026thinsp;=\u0026thinsp;0.85\u0026ndash;1.00; age 40+: OR\u0026thinsp;=\u0026thinsp;0.29, CI\u0026thinsp;=\u0026thinsp;0.26\u0026ndash;0.32) and higher for women with secondary (OR\u0026thinsp;=\u0026thinsp;1.34, CI\u0026thinsp;=\u0026thinsp;1.23\u0026ndash;1.45) or higher education (OR\u0026thinsp;=\u0026thinsp;1.78, CI\u0026thinsp;=\u0026thinsp;1.54\u0026ndash;2.05). Wealthier women were more likely to use ANC services, with odds increasing progressively from poorer (OR\u0026thinsp;=\u0026thinsp;1.31) to richest quintile (OR\u0026thinsp;=\u0026thinsp;1.66). Urban residence (OR\u0026thinsp;=\u0026thinsp;1.15), Christian religion (OR\u0026thinsp;=\u0026thinsp;1.10), and employment (OR\u0026thinsp;=\u0026thinsp;1.13) were positively associated with ANC utilisation. Regional variations were pronounced, with higher odds in the South West (OR\u0026thinsp;=\u0026thinsp;2.75) and South East (OR\u0026thinsp;=\u0026thinsp;1.91), and lower odds in North East (OR\u0026thinsp;=\u0026thinsp;0.53) and North West (OR\u0026thinsp;=\u0026thinsp;0.52) relative to North Central. Mobile phone ownership (OR\u0026thinsp;=\u0026thinsp;1.30) and internet use (OR\u0026thinsp;=\u0026thinsp;1.33) were positively associated with ANC visits. Women who had heard of family planning via social media (OR\u0026thinsp;=\u0026thinsp;1.26) and had heard of or used oral rehydration solution (OR\u0026thinsp;=\u0026thinsp;1.62) also had higher odds of adequate ANC. Knowledge of the ovulatory cycle (OR\u0026thinsp;=\u0026thinsp;1.11), belief that preventive medicine keeps the mother healthy (OR\u0026thinsp;=\u0026thinsp;1.35), and belief that malaria can be cured (OR\u0026thinsp;=\u0026thinsp;1.20) were independently associated with higher ANC utilisation.\u003c/p\u003e \u003cp\u003eSimilar patterns were observed for health facility delivery. Education, wealth, urban residence, and partner\u0026rsquo;s education were all positively associated with facility delivery. Mobile phone ownership (OR\u0026thinsp;=\u0026thinsp;1.34), exposure to family planning via social media (OR\u0026thinsp;=\u0026thinsp;1.24), awareness of ORS (OR\u0026thinsp;=\u0026thinsp;1.46), knowledge of the ovulatory cycle (OR\u0026thinsp;=\u0026thinsp;1.16), and belief that preventive medicine keeps the mother healthy (OR\u0026thinsp;=\u0026thinsp;1.65) were significantly associated with higher odds of facility delivery.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study demonstrate that digital exposure and knowledge-based empowerment are critical determinants of maternal health behaviour among married women in Nigeria. Specifically, access to health information and health knowledge significantly influence the utilisation of maternal health services such as antenatal care and health facility delivery. These findings reinforce the growing body of literature that emphasises the transformative role of digital technologies in promoting positive health behaviours in low- and middle-income countries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results further indicate that sociodemographic characteristics significantly shape maternal health behaviour in Nigeria. Variables such as age, education, wealth status, residence, geopolitical region, religion, household size, employment status, and partner\u0026rsquo;s educational level were found to significantly influence women\u0026rsquo;s use of antenatal care services. These findings align strongly with the Social Determinants of Health framework, which posits that health outcomes are influenced by structural conditions such as education, income, occupation, and geographic location [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Women with higher socioeconomic status, greater educational attainment, and urban residence generally possess greater access to health services and health information, thereby increasing their likelihood of engaging in preventive health behaviours. The strong influence of wealth, education, and geographic region observed in this study further highlights persistent inequalities in access to digital health resources across Nigeria.\u003c/p\u003e \u003cp\u003eIn contrast, women living in rural areas and economically disadvantaged communities, particularly in northern Nigeria, remain relatively marginalised. This pattern reflects the digital divide, which describes the unequal distribution of access to digital technologies across social groups [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The digital divide has important implications for maternal health outcomes because women without access to digital technologies are less likely to receive health information that motivates preventive health behaviours. This finding emphasises the need for targeted digital inclusion initiatives that specifically address the needs of rural, low-income, and less-educated women in Nigeria.\u003c/p\u003e \u003cp\u003eThe study findings demonstrate that digital exposure, including mobile phone ownership and internet use, is positively associated with both ANC utilisation and health facility delivery. Women who owned mobile phones had 1.30 times higher odds of adequate ANC utilisation and 1.34 times higher odds of facility delivery compared to those without phones. These findings are consistent with evidence from Nigeria and Sub-Saharan Africa showing that mobile phone ownership enhances women\u0026rsquo;s access to maternal health information and facilitates timely utilisation of healthcare services [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The positive association between mobile phone ownership and health facility delivery further suggests that mobile health (mHealth) interventions can be effective tools for promoting safe delivery practices.\u003c/p\u003e \u003cp\u003eWomen who were exposed to digital health information, such as messages about family planning through SMS or social media and information about ORS, were significantly more likely to utilise ANC services. This aligns with evidence from DHS analyses in Sub-Saharan Africa showing that women exposed to health information through media and communication channels are more likely to attend recommended antenatal care visits [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, access to health information enhances maternal health literacy and strengthens women\u0026rsquo;s perception of the importance and benefits of preventive maternal healthcare services [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKnowledge-based empowerment indicators, such as knowledge of the ovulatory cycle, belief in the benefits of preventive medicine, and understanding that malaria can be cured, were independently associated with increased utilisation of ANC services [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Broader health literacy, including understanding the importance of preventive medicine and recognising treatable conditions such as malaria, has been shown to improve women\u0026rsquo;s perceptions of susceptibility to pregnancy-related complications and the benefits of skilled maternal care [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence from maternal health studies in LMICs shows that skilled birth attendants and access to emergency obstetric services are among the most effective interventions for preventing maternal and neonatal deaths [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Evidence from Nigeria further highlights the importance of institutional delivery, as findings from NDHS studies show that a substantial proportion of births still occur outside health facilities [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Multilevel analyses of the 2018 NDHS reveal that health facility delivery is strongly associated with prior ANC attendance, maternal education, and household wealth [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Digital exposure and knowledge-based empowerment significantly increase women\u0026rsquo;s likelihood of delivering in health facilities, suggesting that digital health interventions can play an important role in promoting safe childbirth practices.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these findings suggest that behaviour change interventions should focus on expanding digital inclusion among women. Programs that promote mobile phone ownership, increase internet access, and support digital literacy can significantly enhance women\u0026rsquo;s exposure to health information [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, initiatives that encourage women\u0026rsquo;s use of mobile platforms for communication, financial transactions, and information seeking can strengthen their engagement with digital health services [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Expanding women\u0026rsquo;s digital access and literacy represents a critical pathway for enhancing health knowledge, promoting preventive health behaviours, and ultimately improving maternal health outcomes in developing countries [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study examined the associations between sociodemographic characteristics, digital exposure, knowledge-based empowerment, and maternal health behaviour among married women in Nigeria using the 2018 NDHS. The findings demonstrate that maternal health behaviour, particularly ANC utilisation and health facility delivery, is influenced by a complex interaction of structural, technological, and cognitive factors. Sociodemographic characteristics such as education, wealth status, residence, and partner\u0026rsquo;s educational level significantly shape women\u0026rsquo;s access to healthcare services through digital exposure. Digital exposure, measured through mobile phone ownership, internet use, social media engagement, and mobile financial transactions, plays an important role in promoting positive maternal health behaviours. In addition, knowledge-based empowerment emerged as a critical pathway through which digital exposure influences health behaviour. Women who possess accurate reproductive health knowledge and positive beliefs about preventive medicine are more likely to utilise ANC services and deliver in health facilities.\u003c/p\u003e \u003cp\u003eThese findings suggest that effective behaviour change interventions should adopt an integrated approach that combines digital health strategies with women\u0026rsquo;s empowerment initiatives. Expanding digital access, promoting digital literacy, and strengthening women\u0026rsquo;s access to reproductive health information can significantly improve maternal health outcomes. By harnessing the relationships between sociodemographic characteristics, digital exposure, and knowledge-based empowerment, policymakers and health practitioners can design more effective behaviour change interventions that improve maternal health and accelerate progress toward SDGs 3 and 5 in Nigeria by 2030 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntenatal care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnumeration area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFederal Capital Territory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Belief Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInformation and Communication Technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow- and middle-income country\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emHealth\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMobile health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNigeria Demographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Population Commission\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\"\u003eORS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral rehydration solution\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProbability proportional to size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSustainable Development Goal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial Determinants of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort message service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTechnology Acceptance Model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study is based on secondary analysis of publicly available, de-identified data from the 2018 Nigeria Demographic and Health Survey (NDHS), conducted by the National Population Commission (NPC) of Nigeria in collaboration with ICF International. Ethical approval for the NDHS was obtained by the survey administrators from the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. All participants provided written informed consent prior to participation in the original survey. As this study involves secondary analysis of anonymised data with no direct contact with human participants, additional ethics approval was not required. Data access was obtained through registration with the DHS Program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.dhsprogram.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.dhsprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable. This manuscript does not contain data from any individual person in any identifiable form.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of an MSc Dissertation in Demography and Social Statistics at Covenant University, Ota, Nigeria.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.O. conceptualised the study, supervised the research, contributed to the theoretical framework, and reviewed and revised the manuscript. A.E. conducted the literature review, performed the data analysis, drafted the original manuscript, and prepared the final version for submission. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the DHS Program for providing access to the 2018 NDHS dataset, and Covenant University, Ota, for institutional support.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAuthors\u0026rsquo; information\u003c/b\u003e \u003c/p\u003e \u003cp\u003eM.O. is a Senior Lecturer in the Department of Economic \u0026amp; Development Studies and a member of the Public-Private Partnership Research Cluster at Covenant University, Ota, Nigeria, with research interests in maternal and child health, population studies, and development economics. A.E. is a recent MSc graduate in Demography and Social Statistics from Covenant University, Ota, Nigeria, with research interests in digital health, maternal health behaviour, and health equity.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are publicly available through the DHS Program repository. Data can be requested and downloaded at no cost following registration at: https://dhsprogram.com/data/available-datasets.cfm\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUN-DESA. The Sustainable Development Goals Report 2023: Special Edition [Internet]. United Nations; 2023 [cited 2023 Dec 16]. (The Sustainable Development Goals Report). 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MIS Q. 2003;27(3):425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/30036540\u003c/span\u003e\u003cspan address=\"10.2307/30036540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Maternal health behaviour, Antenatal care utilisation, Facility-based delivery, Digital health, Mobile health, Health literacy, Knowledge-based empowerment, Nigeria, NDHS, Low- and middle-income countries","lastPublishedDoi":"10.21203/rs.3.rs-9246834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9246834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMaternal health behaviour is a key determinant of maternal and child health outcomes in Nigeria, yet disparities in the utilisation of essential services persist. While socioeconomic factors remain important predictors, the role of digital exposure and knowledge-based empowerment in shaping women\u0026rsquo;s health-seeking behaviour is increasingly recognised. This study examines the influence of sociodemographic characteristics, digital exposure, and knowledge-based empowerment on maternal health behaviour among married women in Nigeria, with implications for achieving Sustainable Development Goals (SDGs) 3 and 5.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study analysed cross-sectional data from the 2018 Nigeria Demographic and Health Survey (NDHS). The sample comprised 28,888 women aged 15\u0026ndash;49 years who were married or cohabiting and had a recent birth at the time of data collection. Outcome variables were the number of antenatal care (ANC) visits and the place of delivery for the most recent birth. Explanatory variables included sociodemographic characteristics, digital exposure indicators, and knowledge-based empowerment measures. Multivariable logistic regression models were used to estimate adjusted associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eANC utilisation was significantly associated with age, education, wealth, residence, region, religion, household size, employment, occupation, and partner\u0026rsquo;s characteristics. Digital exposure variables (mobile phone ownership, internet use, frequency of use, and mobile financial transactions) were positively associated with ANC visits. Knowledge-based empowerment factors, including exposure to family planning information via SMS and social media, and key health knowledge indicators, were also significant predictors. Similar patterns were observed for place of delivery, with most sociodemographic, digital exposure, and knowledge-based empowerment variables significantly influencing the likelihood of facility-based delivery.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDigital exposure and knowledge-based empowerment complement traditional socioeconomic determinants of maternal health behaviour. Policies that expand digital access and strengthen women\u0026rsquo;s health knowledge, alongside efforts to reduce structural inequalities, are essential for improving maternal health service utilisation and accelerating progress toward SDGs 3 and 5 in Nigeria.\u003c/p\u003e","manuscriptTitle":"Leveraging Sociodemographic, Digital Exposure, and Knowledge-Based Empowerment Factors to Improve Maternal Health Behaviour Among Married Women in Nigeria: A Cross-Sectional Analysis of the 2018 NDHS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 20:57:12","doi":"10.21203/rs.3.rs-9246834/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T14:55:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T14:52:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T02:43:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T02:43:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-03-27T16:09:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0be7a046-9d8e-4f35-ad64-6bec224627cd","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T20:57:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 20:57:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9246834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9246834","identity":"rs-9246834","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}