The Interplay of Digital Access and Socioeconomic Inequality in Maternal Health Service Utilization in Togo: Evidence from the Multiple Indicator Cluster Survey using Survey-Weighted Analysis

The Interplay of Digital Access and Socioeconomic Inequality in Maternal Health Service Utilization in Togo: Evidence from the Multiple Indicator Cluster Survey using Survey-Weighted Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Interplay of Digital Access and Socioeconomic Inequality in Maternal Health Service Utilization in Togo: Evidence from the Multiple Indicator Cluster Survey using Survey-Weighted Analysis Yendouname Kandjoni, Samadou Tchakondo, Sangénis Assogba Ayao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8635479/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Maternal mortality continues to cause a significant public health challenge in Togo, where inequalities in access, continue to reflect both socioeconomic and digital divides. With the rapid expansion of mobile and internet technologies, understanding how these factors interact with traditional determinants such as education, wealth, and residence is a crucial need for designing and implementing health interventions for all. Hence, this study aimed to analyze the influence of digital access (media exposure, internet use, and mobile phone use) after controlling socioeconomic and demographic characteristics, on maternal health-seeking behaviors such as Antenatal Care (ANC ≥ 4 visits), facility delivery, and Post Natal Care (PNC) within 48 hours. Methods This study analyzed Togo’s latest Multiple Indicator Cluster Survey (MICS) data available (2017). A total of 7,326 weighted observations were analyzed using survey-weighted binary logistic regression to identify predictors of maternal health service utilization. An interaction analysis and sensitivity analysis were performed to evaluate the variation of significance in place of residence as well as the robustness findings. All models accounted for the complex sampling design and adjusted for potential confounders. Results Overall, 57.1% of women had four or more ANC visits, 79.6% delivered in a health facility, and 66.7% received PNC within 48 hours. After adjustment, mobile phone use was strongly associated with adequate ANC utilization (AOR = 1.89; 95% CI: 1.44–2.47; p < 0.001). For facility delivery, women with primary [Adjusted Odds Ratio (AOR) = 1.79; 95% CI: 1.20–2.67; p = 0.0046] and secondary or higher education (AOR = 3.97; 95% CI: 2.17–7.25; p < 0.001) had significantly higher odds compared to those with no education. Urban women (AOR = 3.34; 95% CI: 1.34–8.34; p = 0.010), those in richer quintiles were more likely to deliver in facilities. For PNC, media exposure (AOR = 0.72; 95% CI: 0.53–0.96; p = 0.026), higher parity (AOR = 0.66; 95% CI: 0.44–0.99; p = 0.044), and lower education levels were associated with lower PNC attendance within 48 hours. Sensitivity and interaction analyses confirmed the robustness of the main findings. Conclusion Digital access influences, but does not replace, the impact of education, wealth, and residence on maternal health-seeking behaviors. Expanding mobile connectivity and targeted digital literacy, especially among poorer and rural women, could strengthen equitable access to maternal health services across Togo. Trial Registration Not applicable. Maternal health facility delivery digital access media exposure socio-economic inequality Togo MICS Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Maternal mortality continues to cause a significant challenge to global health. Reduce the global maternal mortality ratio to fewer than 70 deaths per 100,000 live births by the year 2030 is one of the Sustainable Development Goal (SDG) targets [ 1 ]. Evidence shows that essential health-seeking behaviours such as adequate antenatal care (ANC), skilled attendance during delivery in health facilities, and timely postnatal care (PNC) are critical in reducing maternal deaths [ 2 , 3 ]. However, in many low- and middle-income countries (LMICs), including Togo, the uptake of these services remains below optimal levels and is marked by inequalities [ 4 ]. Data from Togo’s 2017 MICS show that only about half (51%) of women have attended four or more ANC visits during their most recent pregnancy [ 5 ]. Skilled health personnel attended 79.2% of births, and PNC coverage for mothers is about 85.7%, but the figure was lower in rural areas (72.5%) [ 5 ]. The inequalities as disparities by location and socioeconomic status are evident: 72% of women in urban areas had ≥ 4 ANC visits compared to just 49% in rural areas, and the wealthiest women were far more likely to receive adequate ANC compared to those in the poorest quintile [ 5 ]. More importantly, while the MICS reports these maternal health indicators, it does not present detailed statistics on internet use among pregnant women, leaving behind, an important research gap on digital access and maternal care in Togo. The exposure to mass media (radio, television, and newspapers) has long time been recognized as a strong and valuable predictor of maternal health service utilization. Many studies on health seeking behaviors, including systematic reviews, indicate that women with regular media exposure are more likely to initiate ANC early, to attend at least four ANC visits, to deliver with skilled providers in health facilities, and to seek postnatal services [ 6 – 8 ]. Added to media exposure, determinants such as maternal age, number of prior births, educational attainment, household wealth, rural–urban residence, geographical region, and insurance coverage consistently influence health-seeking behaviours including maternal health services [ 9 – 11 ]. For Togo, a very recent 2023 study has shown how socioeconomic and cultural disparities, particularly those related to wealth and education, continue to drive inequalities and disparities in ANC attendance and facility-based deliveries [ 4 ]. In contrast to mass media, the impact of internet and mobile phone use on maternal health behaviours remains under explored, particularly in the Togolese context. With the rapid expansion of mobile phone ownership and internet connectivity across Sub-Saharan Africa, digital platforms now, present new opportunities for disseminating health information [ 12 , 13 ]. Factors such as mobile phone ownership, computer or tablet use, and combined exposure to traditional and digital media are therefore increasingly relevant for understanding maternal health service utilization. This study addresses these gaps by using Togo’s latest MICS data (2017) to examine how internet and mobile use, and traditional media exposure, along with key sociodemographic and economic factors (age, parity, education, wealth, residence, and region), shape maternal health-seeking behaviours, namely: attending at least four ANC visits, delivering in a health facility with skilled assistance, and receiving PNC within 48 hours of birth. In addition to looking at independent associations, we look at whether the effect of digital access changes based on where someone lives (Urban, rural) or how much money they have (wealth index), in interaction analyses. We test the idea that connectivity could reduce or exacerbate existing socioeconomic gaps. We also test the strength of our results by using different definitions of digital exposure, like owning a laptop or mobile phone, in sensitivity analyses. METHODS Study design, data source and study population This study is based on a secondary data analysis of the MICS-6 (Multiple Indicator Cluster Survey) conducted in Togo in 2017, a nationally representative cross-sectional survey of households, carried out from July to October 2017 by the Togolese Bureau of Statistics in collaboration with United Nation Child’s Fund (UNICEF). The survey used a two-stage stratified sampling design to select 7,916 households in urban and rural areas. Initially, primary sampling units (PSUs or clusters) were chosen from the 2010 census database, and then households were selected within each PSU. From these households, a total of 7,657 women aged 15–49 was interviewed, covering modules on maternal and child health, women’s reproductive behavior, media and internet use, among other indicators. The dataset includes information on births, prenatal care, hospital deliveries and postnatal care, as well as exposure variables such as media and Internet use. Women aged 15–49 years who had a live birth in the two years preceding the survey were included. All women not meeting these two criteria was excluded from the study. This restriction reduces recall bias for maternal health-seeking variables and is consistent with standard MICS and DHS analytical practices [ 10 , 14 ]. Variables definition Outcome variables The following maternal health-seeking behaviours (Table 1) were created (binary indicators) based on the raw survey variables Independent variables / covariates Based on past literature and theoretical framework, the following independent (explanatory) variables in Table 1 are included. Table 1 Definitions and coding of outcome and independent variables Variable type Variable Original / Source variable (Code) Definition / Coding Outcome variables ANC ≥ 4 visits ANC_Visits Coded Yes (1), No (0) Facility delivery Facility delivery Coded Yes (1), No (0) Postnatal care within 48 hours PNC_48H Coded Yes (1), No (0) Independent variables / covariates Media exposure Media_Exposure Composite measure of exposure to radio, television, and newspapers Coded Yes (1), No (0) Internet use MT9 Coded Yes (1), No (0) Laptop use MT4 Coded Yes (1), No (0) Mobile phone use MT11 Coded Yes (1), No (0) Women’s age group Age_Cat 15–19 to 45–49 years Parity Parity_cat categorized into parity groups Education level welevel Highest level of education Wealth index windex5 (1 = poorest to 5 = richest) Place of residence HH6 Urban or Rural Region / County HH7 Administrative region or county of residence These definitions are consistent with the Togo 2017 MICS report’s definitions [ 5 , 15 ]. Sample Weights and Survey Design MICS uses a complex survey design with stratification, clustering, and sampling weights. In our analysis, “wmweight” was the women’s sample weight variable; in this dataset it is already scaled so no further rescaling is done. “PSU” was the primary sampling unit (cluster) variable. “stratum” was the stratification variable. All analyses were conducted using survey-weighted methods that account for weight, cluster, and strata to produce estimates representative of the national population with correct standard errors. Data Preparation Before starting the analysis, several steps were taken to ensure that the data was of good quality and useful. First, the SPSS dataset was imported into SAS OnDemand using the PROC IMPORT DBMS = SAV procedure. Next, the names, types, and missing data patterns of the variables were examined using the CONTENTS and FREQ procedures. A subset of relevant variables was then selected, and missing data indicators were created for key variables, including ANC visits, facility delivery, PNC, media exposure, and internet use. The binary outcome variables were coded as previously described, and finally, the completeness of the design variables (wmweight, PSU and stratum) was checked, with observations lacking any design or key outcome variables being excluded from the dataset. Analytic samples and differing sample sizes. There were 7,657 women (read observations) in the first dataset. After removing records with missing values or weights that weren't positive and weighting the rest, 7,326 observations were left for descriptive analyses (the sum of the weights was 7,326). The number of observations available for multivariate models varied depending on the outcome variable (more than 4 antenatal visits, facility delivery, and PNC within 48 hours), with each variable exhibiting a different missing data profile. Specifically shown in Table 2. The number of clusters/PSUs used in models also varied slightly between outcomes due to the exclusion of records with missing outcome or covariate information or missing cluster identifiers for those records. The main analyses therefore used a full-case approach for each outcome separately (list-based removal of observations not containing the outcome or any covariate included in that model), which produced different analytical sample sizes for each outcome. Table 2 Summary of read vs used observations and sum of weights for each outcome Outcome Observations read Observations used (model) Sum of weights read Sum of weights used Number of clusters (PSUs) used ANC visits > 4 7,657 1,877 7,326 1,870.185 408 Facility delivery 7,657 1,955 7,326 1,935.059 408 PNC within 48 hours 7,657 1,521 7,326 1,542.231 401 Statistical Analysis Sample Size consideration The sample consists of approximately 7,657 women aged 15–49 (women’s file in Togo MICS 2017 had 7,657 women) [ 8 ]. Given prevalence rates from MICS on outcomes, this sample is adequate to detect associations with moderate effect sizes (OR ~ 1.3–1.5), controlling for several covariates with acceptable precision. After weighting the total sample size was 7326, shown in the Table 2 above. The analysis was conducted as per the following steps: Descriptive statistics We used the SAS PROC SURVEYFREQ procedure to find the weighted prevalence and 95% confidence intervals of exposures (media exposure, Internet use and laptop/mobile computer use) and outcomes (ANC ≥ 4, facility delivery, and PNC within 48 hours). We incorporated survey design variables: stratum as stratification variable, PSU as cluster variable and wmweight as sampling weight. Cross-tabulations and Chi-square Exposures compared to outcomes: cross-tabulations (Exposure against ANC ≥ 4; facility delivery; PNC_48H) employing weighted chi-square tests to evaluate crude associations. Multivariable survey-weighted logistic regression PROC SURVEYLOGISTIC used for each outcome variable: Model 1(ANC_Visits), Model 2: Facility delivery and Model 3 (PNC_48H). Independent variables and covariate of the models: use of the internet, exposure to media, age, number of children, education, wealth, place of residence, region, and use of mobile phones. Reference categories were defined as first levels of class variables (lowest education level, lowest wealth quintile, rural residence) to estimate adjusted odds ratios (aOR) and 95% confidence intervals. Interaction / Effect modification To investigate whether the associations between exposures (media exposure, Internet use) and outcomes differed across subgroups, interaction analyses were conducted by incorporating interaction terms between the main exposures and specific sociodemographic variables (the interaction between internet use and place of residence on ANC visits was assessed to determine whether the association varies by place of residence). Sensitivity analyses To assess the robustness of the results in the face of sample selection and missing data, sensitivity analyses were performed (Alternative definitions of exposure: use of a laptop (MT4), mobile phone use instead of use of the Internet). Graphical analysis SAS PROC SGPLOT was used to produce graphs: prevalence bar charts with a 95% confidence interval, grouped bar charts, line graphs, forest plots of adjusted odds ratios and a heat map to identify patterns and interactions between maternal health care seeking behaviors and exposures. RESULTS Characteristics of the Study Population Socio-demographic and economic characteristics A total of 7,326 women aged 15–49 years who had a live birth within the two years preceding the survey were included in the analysis. The mean age of respondents was 29.5 years. The majority of women 52.6% resided in rural areas. Regarding education level, 27.5% of women had no formal education, 32.6% had completed primary education and 40.0% has secondary or higher education. The distribution of household wealth was relatively homogeneous between quintiles (Table 3). Table 3 Distribution of sociodemographic characteristics among women (Weighted). Variable Category Weighted Percent (%) 95% Confidence Interval Residence Urban 47.4 (44.11–50.73) Rural 52.6 (49.27–55.89) Education level No education 27.5 (24.85–30.09) Primary 32.6 (30.88–34.26) Secondary or higher 40.0 (37.73–42.19) Wealth index Poorest 16.1 (13.69–18.54) Poorer 17.6 (15.49–19.77) Middle 19.2 (17.11–21.22) Richer 22.6 (19.90-25.25) Richest 24.5 (21.87–27.17) Age group (years) 15–19 19.9 (18.76–21.01) 20–24 15.8 (14.66–16.87) 25–29 16.6 (15.48–17.74) 30–34 14.6 (13.66–15.53) 35–39 13.6 (12.61–14.50) 40–44 11.0 (10.28–11.80) 45–49 8.5 (7.86–9.22) Parity 1–2 births 39.9 (38.08–41.64) 3 or more births 60.1 (58.36–61.92) Region Maritime 15.3 (13.15–17.51) Plateaux 22.7 (19.88–25.46) Centrale 8.6 (7.30–9.88) Kara 10.7 (8.73–12.67) Savanes 11.9 (9.90-13.89) Lomé Commune 15.9 (13.41–18.49) Golfe Urbain 14.9 (12.12–17.61) Women’s age (continuous) Mean (years) 29.5 (29.27–29.74) Median (years) 28.3 (27.84–28.70) Proportion of media, technology exposure, and Maternal health services utilization Overall, 59.2% of women reported being exposed to media. Internet use was relatively low 14.7%, while 56.6% reported owning or using a mobile phone. Laptop use was significantly lower (Table 4). As shown in Table 4, the use of maternal health services was variable. Approximately 57.1% of women received at least four antenatal consultations. Facility delivery was more prevalent, with 79.6% of women, while 66.7% received postnatal care within 48 hours of delivery. Table 4 Weighted proportion of media exposure/internet use and maternal health services Variable Weighted Prevalence (%) 95% Confidence Interval Use of internet 14.7 13.18–16.22 Use of mobile phone 56.6 54.05–59.25 Use of laptop 9.3 8.23–10.30 Media exposure 59.2 56.27–62.14 ≥ 4 ANC visits 57.1 53.82–60.28 Facility delivery 79.6 76.00-83.17 PNC within 48 hours 66.7 63.38–69.96 Bivariate Analysis Patterns of antenatal care use by age Figure 1 shows that the percentage of women who made at least four ANC visits varies depending on their age group. Younger women (15 to 19 years old) and older women (45 to 49 years old) had lower ANC coverage, but the middle-aged groups (25 to 34 years old) had slightly higher percentages of women who went to four or more ANC visits Associations between media and internet exposure and the use of maternal healthcare Women with internet exposure had a higher prevalence of facility delivery, 98.5% (Rao-Scott χ²=57.24, p < 0.001). Similarly, media exposure was associated with a higher prevalence of facility-delivery (87.2%, Rao-Scott χ²=47.08, p < 0.001) (Supplementary Table S1 ). Women with internet access were more likely to have attended at least four ANC visits (77.0%; p < 0.001), and those exposed to media presented a similar positive trend (64.5%; p < 0.001). A proportion of (65.2%; p = 0.76) of Internet users attended PNC within 48h, while that, among women with media exposure was (64.6%; p = 0.078) (Supplementary Table S1 ). Patterns of antenatal care utilization across wealth quintiles and internet use Figure 2 presents a heatmap showing the proportion of women who attended at least four ANC visits, across different wealth quintiles and levels of internet use. Among women without internet access, ANC attendance was highest in the poorest quintile and progressively increased with wealth, reaching the highest levels in the richest quintile. On the other hand, ANC coverage among internet users seemed to be fairly constant across all income levels, exhibiting moderate-to-high utilisation irrespective of socioeconomic background. Overall, the results indicate that wealth disparities in the use of prenatal care services are more pronounced among women without internet access, while internet use can help mitigate some of these inequalities by promoting more even participation in prenatal care services across all economic strata. Multivariate analysis Interaction of wealth status and internet use on facility delivery Figure 3 shows how facility delivery is shaped by the interaction of wealth quintile and internet use. In the poorest quintile (Q1) of women, those without internet access gave birth in healthcare facilities at a significantly higher rate (100%) compared to those with internet access (51.7%). In contrast, within the second quintile (Q2), internet users were more likely to deliver in a facility (66.9%) than those without internet access (49.1%). For women in the middle to high income quintiles (Q3 to Q5), the rate of hospital births stayed high (over 88%) in both groups, with only small differences between internet users and non-users. Predictors of adequate antenatal care (ANC ≥ 4 Visits) The regression results presented in the table indicate that mobile phone usage serves as a robust and significant predictor of sufficient antenatal care visits (AOR = 1.889; p < 0.0001). Media exposure showed a marginally significant result (AOR = 1.332; p = 0.0505). Women aged 25–29 and 30–34 were significantly more likely to achieve adequate ANC visits compared to those aged 15–19. Other age groups did not show significant differences. Education and wealth status did not show significant effects after adjustment. Urban residence and higher parity were also not significant predictors. Regional variation was a predictor. Overall, as shown in Table 5 access to mobile phones, age, and regional variation are key predictors to have an adequate antenatal care. Socioeconomic status had less impact after adjustment (Table 5). Predictors of Facility Delivery When we controlled potential confounders, many factors showed significant associations with facility delivery. Women who owned a mobile phone had higher odds of delivering in a health facility (AOR = 1.58; p = 0.016). Likewise, urban women were more than three times as likely to deliver in a facility as those residing in rural areas. Education and wealth index became significant predictors for facility delivery. Although parity showed a marginal negative association (AOR = 0.58; p = 0.051), Internet use, media exposure, and regional variation were not associated with facility delivery after we did the adjustment did the adjustment (p > 0.05) (Table 5). Predictors of Postnatal Care (PNC within 48 Hours) As shown in Table 5 We found important predictors of PNC within 48 hours. Women exposed to media were hardly going for timely PNC compared to those who were not exposed (AOR = 0.72; p = 0.026). We found an inverse association about Education and parity too. Other factors, including internet use, mobile phone ownership, wealth, residence, and age, were not significantly associated with early PNC. The results revealed an unusual trend. Media exposure, higher education, and higher parity were associated with lower odds of receiving postnatal care within 48 hours. Table 5 Adjusted Odds Ratios (aOR) for Antenatal Care (ANC ≥ 4), Facility Delivery, and Postnatal Care (PNC within two days) Predictors ANC ≥ 4 visits Facility delivery PNC within 2 days aOR 95% CI p-value aOR 95% CI p-value aOR 95% CI p-value Internet use (Yes vs No) 1.249 0.757–2.059 0.384 2.07 0.43–9.83 0.361 0.73 0.47–1.13 0.152 Media exposure (Yes vs No) 1.332 0.999–1.776 0.051 1.04 0.74–1.45 0.842 0.72 0.53–0.96 0.026* Mobile phone use (Yes vs No) 1.889 1.444–2.472 < 0.001** 1.58 1.09–2.29 0.016* 1.17 0.86–1.61 0.313 Age 20–24 vs 15–19 1.227 0.694–2.169 0.481 0.75 0.36–1.53 0.425 0.95 0.54–1.66 0.843 Age 25–29 vs 15–19 1.815 1.046–3.151 0.034* 0.82 0.37–1.81 0.629 0.97 0.52–1.82 0.927 Age 30–34 vs 15–19 1.968 1.065–3.638 0.031* 1.08 0.45–2.59 0.871 0.97 0.49–1.89 0.919 Age 35–39 vs 15–19 1.533 0.783–3.004 0.212 0.86 0.37–2.02 0.726 0.93 0.43–2.02 0.859 Age 40–44 vs 15–19 1.878 0.926–3.808 0.081 1.26 0.50–3.18 0.620 1.58 0.57–4.33 0.378 Age 45–49 vs 15–19 1.018 0.302–3.432 0.977 0.55 0.08–3.67 0.532 0.95 0.22–4.07 0.942 Primary education vs none 1.032 0.769–1.386 0.832 1.79 1.20–2.67 0.005** 0.58 0.39–0.88 0.010* Secondary + education vs none 1.325 0.896–1.959 0.158 3.97 2.17–7.25 < 0.001** 0.66 0.44–0.97 0.035* Wealth: Poorer vs middle 0.734 0.489–1.102 0.135 0.47 0.28–0.77 0.003** 0.91 0.56–1.47 0.692 Wealth: Poorest vs middle 0.847 0.541–1.326 0.467 0.31 0.19–0.52 < 0.001** 0.98 0.57–1.68 0.926 Wealth: Richer vs middle 0.896 0.587–1.368 0.610 2.52 1.18–5.42 0.018* 0.83 0.53–1.30 0.406 Wealth: Richest vs middle 1.204 0.736–1.970 0.458 1.38 0.26–7.47 0.707 1.46 0.87–2.43 0.148 Urban vs rural residence 0.760 0.495–1.167 0.210 3.34 1.34–8.34 0.010* 1.13 0.66–1.94 0.660 Parity ≥ 3 vs 1–2 0.820 0.596–1.130 0.225 0.58 0.33–1.00 0.051 0.66 0.44–0.99 0.044* Region 1.102 1.018–1.193 0.016* 0.93 0.80–1.08 0.349 1.00 0.91–1.09 0.930 Sensitivity analysis To examine the robustness of the findings, we have performed a sensitivity analysis. We substituted laptop use (MT4) for internet use (MT9) in the regression model. Our model remained statistically significant (Likelihood Ratio test, p < 0.0001). The model fit indices (AIC = 2509.5, c-statistic = 0.637) were comparable to the primary analysis. In this specification, laptop use was not significant nor associated with the likelihood of completing at least four ANC visits (AOR = 1.31, p = 0.343). However, other key predictors retained statistical significance. They were consistent with the main model. In Summary, the sensitivity analysis confirmed the robustness of the main findings. We found that laptop use itself was not a significant predictor. But the associations of media exposure, maternal age, and higher education with adequate ANC utilization remained stable across model specifications (Supplementary Table S3). DISCUSSION Our study, which involved Togolese women, investigated the relationship between maternal health care utilization behaviours (ANC ≥ 4 visits, facility delivery, and PNC within 48 hours) and media exposure, internet and mobile phone use, and sociodemographic characteristics. Several interesting patterns emerged, which both align with and diverge from prior work. Below, we discuss key findings, possible explanations, policy implications, limitations, and directions for future research. Main findings Mobile phones stood out, predicted ≥ 4 ANC visits (AOR = 1.889) and facility delivery (AOR = 1.580). Media exposure had a small, borderline positive link with ANC (AOR = 1.332, p = 0.0505). Unexpectedly, media exposure was linked with lower odds of PNC within 48 hours (AOR = 0.716). Internet use looked very strong in crude comparisons but lost significance after we adjusted for sociodemographic factors. These numbers help frame the rest of the discussion. Digital access and maternal health service utilization Mobile phone use was a consistent predictor of maternal health service utilization across our models. Similar benefits have been reported in several low-resource settings, where mobile- and SMS-based interventions improved antenatal care attendance and skilled delivery, including Ghana’s T4MCH program [ 1 , 2 , 16 , 17 ]. Evidence from digital health reviews suggests that simple tools such as SMS and voice calls are often more effective than complex platforms in contexts with limited connectivity and digital literacy [ 17 , 25 , 26 ], which may explain why mobile phone use remained significant while laptop ownership or general internet access did not. Mobile phone use, however, was not associated with early postnatal care. The immediate postpartum period is time-sensitive and often constrained by transport needs, household responsibilities, and health system capacity. While mobile phones can support communication, they are unlikely to overcome these physical and organizational barriers on their own [ 30 , 31 ]. Internet use showed strong associations with facility delivery in unadjusted analyses, but these associations weakened after accounting for education, wealth, and residence, suggesting confounding by socioeconomic advantage. Heatmap patterns nonetheless indicated that antenatal care coverage among internet users was relatively high across all wealth groups, whereas among non-users it increased sharply with wealth. This suggests that internet access may act as a facilitating resource for ANC utilization rather than an independent driver [ 29 ]. Consistent with this, internet-based interventions often require higher literacy and stable connectivity, limiting their effectiveness in low-resource settings [ 25 , 26 ]. Finally, interaction analyses showed no evidence that the association between digital access and antenatal care differed by place of residence. This implies that once basic access is available, mobile-based digital content may offer similar potential benefits in rural and urban settings. Although urban–rural disparities in digital health uptake have been reported elsewhere [ 28 ], our findings suggest that patterns of use may matter more than geographic location alone. Media exposure and mixed associations Media exposure showed a borderline positive association with ANC (aOR = 1.33, p = 0.0505) but was not predictive of facility delivery after adjustment, and interestingly, was negatively associated with PNC within 48 hours (aOR = 0.72). The negative PNC association is unexpected and contrasts with much of the literature that finds a positive role of media in maternal healthcare [ 4 , 18 , 20 ]. Many studies in sub-Saharan Africa and beyond show that exposure to radio, television, newspapers is positively associated with maternal care use (ANC, institutional delivery, PNC) [ 6 , 19 ]. In Nepal, Sharma et al. (2024) also found positive associations of media exposure with ANC, facility delivery, and PNC utilization [ 4 ]. Recent research indicates that numerous pregnant women increasingly depend on phones, social media, and online communities for information [ 27 ]. this may influence their assessment of necessary services and timing. Education, wealth, and urbanization on facility delivery Our results for facility delivery followed a familiar pattern. Education played a major role. Women who had only gone to primary school were more likely to give birth in a health facility (AOR = 1.79), and women who had gone to secondary school or higher were even more likely to do so (AOR = 3.97). This is consistent with what Bobo et al. found. [ 5 , 19 , 20 ]. Wealth created a clear divide as well. Women in the poorest groups were much less likely to deliver in a facility (AOR = 0.47 and 0.31), while richer women were more likely to do so (AOR = 2.52). This reflects what many studies in Africa have already shown [ 5 , 6 , 19 , 20 ]. Living in an urban area also made a difference (AOR = 3.34), likely because of shorter distances and better facility availability. Digital tools helped, but they did not remove these structural inequalities. Age, parity, and regional variation Age and region also shaped service use. Women in their late twenties and early thirties were more likely to reach ANC4+, while adolescents were less likely to do so. It is possibly due to stigma and limited autonomy. Regional differences suggest that some areas have better outreach or easier physical access to services. Parity played a role too. Women with several children were less likely to return for early PNC and showed slightly weaker patterns for facility delivery. They may rely on past experience or simply face more practical constraints at home. Robustness, sensitivity, and interaction analyses Sensitivity analyses using laptop ownership as an alternative indicator of digital access produced results consistent with the main models. Laptop use was not independently associated with adequate ANC utilization, while associations for mobile phone use, maternal age, education, and media exposure remained stable. Interaction analyses showed that neither internet use nor place of residence independently or jointly predicted ANC utilization, suggesting that access alone may be insufficient and that how women engage with digital tools is likely more important. These findings are consistent with the broader digital health literature, which indicates that although web-based platforms, apps, and social media can support maternal health, their impact is often limited by gaps in digital literacy, connectivity, and health system context [ 23 – 25 ]. In low-resource settings, mobile phones, requiring lower bandwidth and offering greater accessibility, tend to be more effective for health communication than laptops or general internet access [ 26 ]. Studies also show that pregnant women commonly rely on mobile-based platforms and social media for information and support [ 27 ]. Overall, our results reinforce the idea that strengthening mobile-based engagement may be more impactful for improving ANC uptake than expanding internet access alone. Continuum of maternal care Our results echo a known pattern of dropout along the continuum of care: many women initiate ANC and deliver at facility, but fewer access early PNC [ 9 , 19 , 21 ]. In their study across 25 SSA countries, only 30% of women received the full recommended package (ANC4+, skilled birth, PNC). The fact that our PNC determinants differ from those of ANC and facility delivery underscores how PNC may face unique barriers (timing constraints, postpartum mobility, health system bottlenecks). Global analyses have similarly highlighted persistent gaps in postnatal care coverage, particularly within the first two days after birth, due to system-level and logistical constraints [ 30 , 31 ]. Strengths and limitations This study has several strengths. It uses nationally representative MICS data, applies appropriate survey weighting and design-based analyses, and incorporates a comprehensive set of sociodemographic covariates. The focus on digital access and media exposure provides a contemporary perspective on maternal health-seeking behaviors in Togo, a context where such factors have been little studied. However, we acknowledge several limitations. The cross-sectional design precludes causal inference. All measures are self-reported and subject to recall error, particularly for the timing of early postnatal care. We did not distinguish between different media channels. Additionally, important factors such as distance to facilities, quality of care, partner influence, and facility readiness were not available in the dataset. Finally, the findings reflect the context of Togo in 2017, although these data remain the most recent nationally representative evidence available. Recommendations and Programmatic Implications Introduce a lightweight digital follow up system (mMaternalCARE) Our findings suggest that many gaps in ANC and early PNC, and even children immunization, come from simple follow-up challenges. Health workers often search paper registers or make manual calls to track missed visits, which is slow and easy to miss. A small, mobile-first digital system, something like Maternal Care could help. The idea is straightforward: automate reminders, flag mothers who miss ANC, PNC, or immunization appointments, and give health workers a clear dashboard so they don’t have to chase lists by hand. The system would rely on SMS and USSD (not full internet) so it works even for women with basic phones. It could also help CHWs prioritize follow-up using factors that came out strongly in our results (parity, wealth, education, region, mobile ownership). This type of tool would reduce missed visits, lighten the workload, and make outreach more systematic, especially in rural and low-resource areas. It also aligns with growing regional efforts to integrate client-tracking systems with DHIS2 and other national platforms. Evidence from mHealth literature confirms that mobile technology can improve maternal health outcomes when well implemented [ 10 , 22 , 23 , 24 ]. In addition to this, improving early postnatal care will likely require targeted health system interventions, including improved postpartum follow-up protocols, extended service availability, and transport support. Media strategies should be reviewed to ensure that messaging around postnatal care is clear, actionable, and supportive. Rural health facility quality must be improved, and also expand infrastructure. These are essential for equitable maternal care. Future research should disaggregate media exposure by channel and intensity, and explore longitudinal or intervention designs to assess causality. Integrating geospatial measures of access and facility capacity could further clarify how digital and structural factors interact to shape maternal health service utilization. CONCLUSION This study explored maternal health service utilization in Togo and the role of digital access, media exposure, and sociodemographic factors in shaping care-seeking behaviors. Mobile phone use emerged as a consistent facilitator of antenatal care attendance and facility delivery, highlighting the value of simple digital tools in resource-constrained health systems. At the same time, the findings underscore important limits. Media exposure was not associated with improved early postnatal care and, in fact, was linked to lower uptake, suggesting that information alone is insufficient. Internet use showed strong crude associations but was largely explained by underlying socioeconomic advantage after adjustment. Across outcomes, structural factors particularly education, wealth, and urban residence remained central determinants of service utilization, while early postnatal care continued to be the weakest point in the maternal care continuum. Overall, these results suggest that digital tools can support maternal health service use, but their impact depends on context. Mobile-based approaches are most effective when combined with accessible services, adequate infrastructure, and supportive social environments. Strengthening mobile engagement, improving postnatal care accessibility, and targeting adolescents and women with lower education levels may help ensure that more women complete the full continuum of maternal care in Togo. Declarations Acknowledgements The authors express their gratitude to the School of Public Health, SRM Institute of Science and Technology (SRM IST), for their guidance and support throughout the study and manuscript submission. We extend our appreciation to the United Nations Children's Fund (UNICEF) team and TOGO UNICEF for providing access to the publicly available datasets utilized in this research. We also thank our colleagues and mentors for their valuable feedback and encouragement, which greatly enhanced the quality of this work. Authors' contributions YK conceptualized the study, designed the methodology, conducted data analysis, interpretation, and manuscript drafting. ST, KSG, SAA, and RST contributed to data analysis and manuscript drafting. Dr GJH offered methodological guidance and substantive revisions to the manuscript. All authors reviewed and approved the final version for submission. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability The data analyzed in this study are publicly available through the UNICEF MICS program. Specifically, this study used data from the Togo 2017 Multiple Indicator Cluster Survey (MICS-6). Access to the dataset can be obtained upon approval of a data request submitted through the UNICEF MICS website (https://mics.unicef.org/surveys). All analyses were conducted using anonymized secondary data in accordance with ethical research standards. Ethical Approval and consent to participate Ethical approval for this study was not required, as it involved secondary analysis of publicly available, anonymized data from the United Nations Child’s Fund (UNICEF) Multiple Indicator Cluster Survey (MICS-5). The Togo MICS obtained ethical approval from institutional review boards (IRBs) and informed consent from all participants at the time of data collection. Competing Interests The authors declare no competing interests. Consent for publication All authors consent to the publication of this manuscript in its current form. References Lee BX, Kjaerulf F, Turner S, et al. Transforming Our World: Implementing the 2030 Agenda Through Sustainable Development Goal Indicators. J Public Health Pol. 2016;37(Suppl 1):13–31. 10.1057/s41271-016-0002-7 . World Health Organization. Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank and the United Nations Population Division. Geneva: WHO; 2019. World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. Geneva: WHO; 2016. Kota K, Chomienne MH, Geneau R, et al. Socio-economic and cultural factors associated with the utilization of maternal healthcare services in Togo: a cross-sectional study. Reprod Health. 2023;20:109. 10.1186/s12978-023-01644-6 . Togo, Profile MICS, Countdown. to 2030. 2017. (MICS 2017, Togo) [Internet]. Available from: https://www.countdown2030.org/wp-content/uploads/2020/09/Togo-MICS-2017.pdf [cited 2025 Sep 15]. Aboagye RG, Seidu AA, Ahinkorah BO, et al. Association between frequency of mass media exposure and maternal health care service utilisation among women in sub-Saharan Africa: implications for tailored health communication and education. PLoS ONE. 2022;17(9):e0275202. Fatema K, Lariscy JT. Mass media exposure and maternal healthcare utilization: evidence from South Asia. J Health Commun. 2020;25(10):825–35. Armier M, et al. Exposure to the mass media and utilization of maternal and child health services in Uganda: an analysis of Uganda demographic health survey data. BMC Public Health. 2021;21(1):234. Fetene SM, Alemu MB, Fentie EA, Haile TG. Continuum of maternal health care utilisation in Sub-Saharan African countries: A positive deviance approach. PLoS ONE. 2025;20(6):e0314779. 10.1371/journal.pone.0314779 . Fetene SM, Fentie EA, Shewarega ES, et al. Socioeconomic inequality in postnatal care utilization among reproductive age women in sub-Saharan African countries with high maternal mortality: a decomposition analysis. BMJ Open. 2024;14:e076453. 10.1136/bmjopen-2023-076453 . Socio-economic inequality in maternal health care utilization in Sub-Saharan Africa. Evidence from Togo [Atake EH. et al ] Int J Health Plann Manage. 2020;36(4):e3083. Betjeman TJ, Soghoian SE, Foran MP. mHealth in Sub-Saharan Africa. Int J Telemed Appl. 2013:482324. World Bank. World Development Indicators – Internet Users (% of population). Washington DC: The World Bank; 2021. UNICEF. Togo 2017 Profile MICS, Maternal and Newborn Health Coverage Indicators. UNICEF /, Countdown. to 2030; 2017. Available from: https://www.countdown2030.org/wp-content/uploads/2020/09/Togo-MICS-2017.pdf [cited 2025 Sep 15]. Multiple Indicator Cluster Surveys (MICS). Datasets and reports. UNICEF. 2017. available from: https://mics.unicef.org/surveys [cited 2025 Sep 15]. De P, Pradhan MR. Effectiveness of mobile technology and utilization of maternal and neonatal healthcare in low and middle-income countries (LMICs): a systematic review. BMC Womens Health. 2023;23:664. 10.1186/s12905-023-02825-y . Nuhu AGK, Dwomoh D, Amuasi SA, et al. Impact of mobile health on maternal and child health service utilization and continuum of care in Northern Ghana. Sci Rep. 2023;13:3004. 10.1038/s41598-023-29683-w . Sharma S, Adhikari B, Pandey AR, et al. Association between media exposure and maternal health service use in Nepal: a further analysis of Nepal DHS-2022. PLoS ONE. 2024;19(3):e0297418. Bobo FT, Asante A, Woldie M, et al. Evaluating equity across the continuum of care for maternal health services: analysis of national health surveys from 25 sub-Saharan African countries. Int J Equity Health. 2023;22:239. 10.1186/s12939-023-02047-6 . Tessema ZT, Yazachew L, Tesema GA, et al. Determinants of postnatal care utilization in sub-Saharan Africa: a meta and multilevel analysis of data from 36 sub-Saharan countries. Ital J Pediatr. 2020;46:175. 10.1186/s13052-020-00944-y . Mekonen EG et al. Only three out of ten women received adequate postnatal care in sub-Saharan Africa: a pooled prevalence study. BMC Pregnancy Childbirth. 2025. Atnafu A, Bisrat A, Kifle M, Taye B, Debebe T. (2016). Mobile health (mHealth) intervention in maternal and child health care: Evidence from resource-constrained settings: A review. Ethiop J Health Dev, 29 (3). Elliot Mbunge MN, Sibiya. Mobile health interventions for improving maternal and child health outcomes in South Africa: a systematic review. Global Health J, 8(3), Pages 103–12, 10.1016/j.glohj.2024.08.002 Kachimanga C, Zaniku HR, Divala TH, et al. Evaluating the Adoption of mHealth Technologies by Community Health Workers to Improve the Use of Maternal Health Services in SSA: systematic review. JMIR Mhealth Uhealth. 2024;12:e55819. Mohamed H, Ismail A, Sutan R, et al. A scoping review of digital technologies in antenatal care: recent progress and applications of digital technologies. BMC Pregnancy Childbirth. 2025;25:153. 10.1186/s12884-025-07209-8 . Jaynes S et al. Systematic Review of the Effect of Technology-Mediated Education Intervention on Maternal Health Outcomes. JOGNN 10.1016/j.jogn.2022.02.005 Giacometti CF et al. Internet use by pregnant women during prenatal care. PMC Einstein (16794508),2024. Rani D, Kumar R, Chauhan N. Study Influencing Factors of Maternal Health and the Role of Internet of Things (IoT) to Improve Maternal Care. SN COMPUT SCI. 2024;5:778. 10.1007/s42979-024-03129-0 . Yu J, Meng S. Impacts of internet use on health inequality: a cross-country study. Front Public Health. 2022;10:935608. World Health Organization. WHO recommendations on postnatal care of the mother and newborn. Geneva: WHO; 2013. UNICEF, World Health Organization. Postnatal care for mothers and newborns: highlights from the World Health Statistics. New York: UNICEF; 2022. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 31 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 08 Feb, 2026 Editor invited by journal 23 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 19 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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05:53:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8635479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8635479/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102528748,"identity":"68070d3b-da45-40d4-91a6-3d25934cf635","added_by":"auto","created_at":"2026-02-12 16:03:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27967,"visible":true,"origin":"","legend":"\u003cp\u003eANC coverage by Age group of the mother\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8635479/v1/6da0f8a20f9d6c47a9e0e9a1.png"},{"id":102746530,"identity":"5c18df2b-5dab-48f3-a755-61b2ef7286db","added_by":"auto","created_at":"2026-02-16 08:58:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30992,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of four or more antenatal care visits (ANC ≥4) by wealth quintile and internet use\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8635479/v1/65faa855fa0cb1cadd9c52b5.png"},{"id":102528750,"identity":"fce3c03f-72ed-42d6-8ca1-f30b95bd6e38","added_by":"auto","created_at":"2026-02-12 16:03:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28601,"visible":true,"origin":"","legend":"\u003cp\u003eFacility delivery (%) by wealth quintile and internet use\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8635479/v1/4399fd99108d606e0aa4d682.png"},{"id":102750427,"identity":"829edcc6-ab4d-484a-8a45-83daffbd0331","added_by":"auto","created_at":"2026-02-16 09:19:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2011828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8635479/v1/59d3da95-5b0d-4943-a63c-cd2346ebc795.pdf"},{"id":102528749,"identity":"42bcf263-bcca-4ef9-a6f0-8df68b1c42ad","added_by":"auto","created_at":"2026-02-12 16:03:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25533,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8635479/v1/c774778b916e9bcca1f7be0a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Interplay of Digital Access and Socioeconomic Inequality in Maternal Health Service Utilization in Togo: Evidence from the Multiple Indicator Cluster Survey using Survey-Weighted Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMaternal mortality continues to cause a significant challenge to global health. Reduce the global maternal mortality ratio to fewer than 70 deaths per 100,000 live births by the year 2030 is one of the Sustainable Development Goal (SDG) targets [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Evidence shows that essential health-seeking behaviours such as adequate antenatal care (ANC), skilled attendance during delivery in health facilities, and timely postnatal care (PNC) are critical in reducing maternal deaths [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, in many low- and middle-income countries (LMICs), including Togo, the uptake of these services remains below optimal levels and is marked by inequalities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData from Togo\u0026rsquo;s 2017 MICS show that only about half (51%) of women have attended four or more ANC visits during their most recent pregnancy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Skilled health personnel attended 79.2% of births, and PNC coverage for mothers is about 85.7%, but the figure was lower in rural areas (72.5%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The inequalities as disparities by location and socioeconomic status are evident: 72% of women in urban areas had\u0026thinsp;\u0026ge;\u0026thinsp;4 ANC visits compared to just 49% in rural areas, and the wealthiest women were far more likely to receive adequate ANC compared to those in the poorest quintile [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. More importantly, while the MICS reports these maternal health indicators, it does not present detailed statistics on internet use among pregnant women, leaving behind, an important research gap on digital access and maternal care in Togo.\u003c/p\u003e \u003cp\u003eThe exposure to mass media (radio, television, and newspapers) has long time been recognized as a strong and valuable predictor of maternal health service utilization. Many studies on health seeking behaviors, including systematic reviews, indicate that women with regular media exposure are more likely to initiate ANC early, to attend at least four ANC visits, to deliver with skilled providers in health facilities, and to seek postnatal services [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Added to media exposure, determinants such as maternal age, number of prior births, educational attainment, household wealth, rural\u0026ndash;urban residence, geographical region, and insurance coverage consistently influence health-seeking behaviours including maternal health services [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For Togo, a very recent 2023 study has shown how socioeconomic and cultural disparities, particularly those related to wealth and education, continue to drive inequalities and disparities in ANC attendance and facility-based deliveries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to mass media, the impact of internet and mobile phone use on maternal health behaviours remains under explored, particularly in the Togolese context. With the rapid expansion of mobile phone ownership and internet connectivity across Sub-Saharan Africa, digital platforms now, present new opportunities for disseminating health information [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Factors such as mobile phone ownership, computer or tablet use, and combined exposure to traditional and digital media are therefore increasingly relevant for understanding maternal health service utilization.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by using Togo\u0026rsquo;s latest MICS data (2017) to examine how internet and mobile use, and traditional media exposure, along with key sociodemographic and economic factors (age, parity, education, wealth, residence, and region), shape maternal health-seeking behaviours, namely: attending at least four ANC visits, delivering in a health facility with skilled assistance, and receiving PNC within 48 hours of birth. In addition to looking at independent associations, we look at whether the effect of digital access changes based on where someone lives (Urban, rural) or how much money they have (wealth index), in interaction analyses. We test the idea that connectivity could reduce or exacerbate existing socioeconomic gaps. We also test the strength of our results by using different definitions of digital exposure, like owning a laptop or mobile phone, in sensitivity analyses.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, data source and study population\u003c/h2\u003e \u003cp\u003eThis study is based on a secondary data analysis of the MICS-6 (Multiple Indicator Cluster Survey) conducted in Togo in 2017, a nationally representative cross-sectional survey of households, carried out from July to October 2017 by the Togolese Bureau of Statistics in collaboration with United Nation Child\u0026rsquo;s Fund (UNICEF). The survey used a two-stage stratified sampling design to select 7,916 households in urban and rural areas. Initially, primary sampling units (PSUs or clusters) were chosen from the 2010 census database, and then households were selected within each PSU. From these households, a total of 7,657 women aged 15\u0026ndash;49 was interviewed, covering modules on maternal and child health, women\u0026rsquo;s reproductive behavior, media and internet use, among other indicators. The dataset includes information on births, prenatal care, hospital deliveries and postnatal care, as well as exposure variables such as media and Internet use.\u003c/p\u003e \u003cp\u003eWomen aged 15\u0026ndash;49 years who had a live birth in the two years preceding the survey were included.\u003c/p\u003e \u003cp\u003eAll women not meeting these two criteria was excluded from the study. This restriction reduces recall bias for maternal health-seeking variables and is consistent with standard MICS and DHS analytical practices [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables definition\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variables\u003c/h2\u003e \u003cp\u003eThe following maternal health-seeking behaviours (Table\u0026nbsp;1) were created (binary indicators) based on the raw survey variables\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent variables / covariates\u003c/h3\u003e\n\u003cp\u003eBased on past literature and theoretical framework, the following independent (explanatory) variables in Table\u0026nbsp;1 are included.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinitions and coding of outcome and independent variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOriginal / Source variable (Code)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefinition / Coding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANC\u0026thinsp;\u0026ge;\u0026thinsp;4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANC_Visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacility delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFacility delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostnatal care within 48 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNC_48H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent variables / covariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedia exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedia_Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposite measure of exposure to radio, television, and newspapers\u003c/p\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaptop use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMobile phone use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoded Yes (1), No (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen\u0026rsquo;s age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge_Cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026ndash;19 to 45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParity_cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecategorized into parity groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewelevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewindex5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1\u0026thinsp;=\u0026thinsp;poorest to 5\u0026thinsp;=\u0026thinsp;richest)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban or Rural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion / County\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdministrative region or county of residence\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\u003eThese definitions are consistent with the Togo 2017 MICS report\u0026rsquo;s definitions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSample Weights and Survey Design\u003c/h3\u003e\n\u003cp\u003eMICS uses a complex survey design with stratification, clustering, and sampling weights.\u003c/p\u003e \u003cp\u003eIn our analysis, \u0026ldquo;wmweight\u0026rdquo; was the women\u0026rsquo;s sample weight variable; in this dataset it is already scaled so no further rescaling is done. \u0026ldquo;PSU\u0026rdquo; was the primary sampling unit (cluster) variable. \u0026ldquo;stratum\u0026rdquo; was the stratification variable.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using survey-weighted methods that account for weight, cluster, and strata to produce estimates representative of the national population with correct standard errors.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Preparation\u003c/h2\u003e \u003cp\u003eBefore starting the analysis, several steps were taken to ensure that the data was of good quality and useful. First, the SPSS dataset was imported into SAS OnDemand using the PROC IMPORT DBMS\u0026thinsp;=\u0026thinsp;SAV procedure. Next, the names, types, and missing data patterns of the variables were examined using the CONTENTS and FREQ procedures. A subset of relevant variables was then selected, and missing data indicators were created for key variables, including ANC visits, facility delivery, PNC, media exposure, and internet use. The binary outcome variables were coded as previously described, and finally, the completeness of the design variables (wmweight, PSU and stratum) was checked, with observations lacking any design or key outcome variables being excluded from the dataset.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalytic samples and differing sample sizes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere were 7,657 women (read observations) in the first dataset. After removing records with missing values or weights that weren't positive and weighting the rest, 7,326 observations were left for descriptive analyses (the sum of the weights was 7,326). The number of observations available for multivariate models varied depending on the outcome variable (more than 4 antenatal visits, facility delivery, and PNC within 48 hours), with each variable exhibiting a different missing data profile. Specifically shown in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eThe number of clusters/PSUs used in models also varied slightly between outcomes due to the exclusion of records with missing outcome or covariate information or missing cluster identifiers for those records. The main analyses therefore used a full-case approach for each outcome separately (list-based removal of observations not containing the outcome or any covariate included in that model), which produced different analytical sample sizes for each outcome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of read vs used observations and sum of weights for each outcome\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\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations read\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservations used (model)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of weights read\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSum of weights used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of clusters (PSUs) used\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC visits\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,870.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,935.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNC within 48 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,542.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eSample Size consideration\u003c/h2\u003e \u003cp\u003eThe sample consists of approximately 7,657 women aged 15\u0026ndash;49 (women\u0026rsquo;s file in Togo MICS 2017 had 7,657 women) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given prevalence rates from MICS on outcomes, this sample is adequate to detect associations with moderate effect sizes (OR\u0026thinsp;~\u0026thinsp;1.3\u0026ndash;1.5), controlling for several covariates with acceptable precision. After weighting the total sample size was 7326, shown in the Table\u0026nbsp;2 above.\u003c/p\u003e \u003cp\u003eThe analysis was conducted as per the following steps:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eWe used the SAS PROC SURVEYFREQ procedure to find the weighted prevalence and 95% confidence intervals of exposures (media exposure, Internet use and laptop/mobile computer use) and outcomes (ANC\u0026thinsp;\u0026ge;\u0026thinsp;4, facility delivery, and PNC within 48 hours). We incorporated survey design variables: stratum as stratification variable, PSU as cluster variable and wmweight as sampling weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCross-tabulations and Chi-square\u003c/h2\u003e \u003cp\u003eExposures compared to outcomes: cross-tabulations (Exposure against ANC\u0026thinsp;\u0026ge;\u0026thinsp;4; facility delivery; PNC_48H) employing weighted chi-square tests to evaluate crude associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable survey-weighted logistic regression\u003c/h2\u003e \u003cp\u003ePROC SURVEYLOGISTIC used for each outcome variable: Model 1(ANC_Visits), Model 2: Facility delivery and Model 3 (PNC_48H).\u003c/p\u003e \u003cp\u003eIndependent variables and covariate of the models: use of the internet, exposure to media, age, number of children, education, wealth, place of residence, region, and use of mobile phones.\u003c/p\u003e \u003cp\u003eReference categories were defined as first levels of class variables (lowest education level, lowest wealth quintile, rural residence) to estimate adjusted odds ratios (aOR) and 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInteraction / Effect modification\u003c/h2\u003e \u003cp\u003eTo investigate whether the associations between exposures (media exposure, Internet use) and outcomes differed across subgroups, interaction analyses were conducted by incorporating interaction terms between the main exposures and specific sociodemographic variables (the interaction between internet use and place of residence on ANC visits was assessed to determine whether the association varies by place of residence).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eTo assess the robustness of the results in the face of sample selection and missing data, sensitivity analyses were performed (Alternative definitions of exposure: use of a laptop (MT4), mobile phone use instead of use of the Internet).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGraphical analysis\u003c/h2\u003e \u003cp\u003e SAS PROC SGPLOT was used to produce graphs: prevalence bar charts with a 95% confidence interval, grouped bar charts, line graphs, forest plots of adjusted odds ratios and a heat map to identify patterns and interactions between maternal health care seeking behaviors and exposures.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of the Study Population\u003c/h2\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003eSocio-demographic and economic characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 7,326 women aged 15\u0026ndash;49 years who had a live birth within the two years preceding the survey were included in the analysis. The mean age of respondents was 29.5 years. The majority of women 52.6% resided in rural areas. Regarding education level, 27.5% of women had no formal education, 32.6% had completed primary education and 40.0% has secondary or higher education. The distribution of household wealth was relatively homogeneous between quintiles (Table 3).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of sociodemographic characteristics among women (Weighted).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeighted Percent (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(44.11\u0026ndash;50.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(49.27\u0026ndash;55.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(24.85\u0026ndash;30.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(30.88\u0026ndash;34.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(37.73\u0026ndash;42.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(13.69\u0026ndash;18.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(15.49\u0026ndash;19.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(17.11\u0026ndash;21.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(19.90-25.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(21.87\u0026ndash;27.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(18.76\u0026ndash;21.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(14.66\u0026ndash;16.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(15.48\u0026ndash;17.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(13.66\u0026ndash;15.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(12.61\u0026ndash;14.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(10.28\u0026ndash;11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(7.86\u0026ndash;9.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(38.08\u0026ndash;41.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 or more births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(58.36\u0026ndash;61.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaritime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(13.15\u0026ndash;17.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlateaux\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(19.88\u0026ndash;25.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentrale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(7.30\u0026ndash;9.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(8.73\u0026ndash;12.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSavanes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(9.90-13.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLom\u0026eacute; Commune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(13.41\u0026ndash;18.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGolfe Urbain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(12.12\u0026ndash;17.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u0026rsquo;s age (continuous)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(29.27\u0026ndash;29.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(27.84\u0026ndash;28.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eProportion of media, technology exposure, and Maternal health services utilization\u003c/h2\u003e\n \u003cp\u003eOverall, 59.2% of women reported being exposed to media. Internet use was relatively low 14.7%, while 56.6% reported owning or using a mobile phone. Laptop use was significantly lower (Table\u0026nbsp;4).\u003c/p\u003e\n \u003cp\u003eAs shown in Table 4, the use of maternal health services was variable. Approximately 57.1% of women received at least four antenatal consultations. Facility delivery was more prevalent, with 79.6% of women, while 66.7% received postnatal care within 48 hours of delivery.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWeighted proportion of media exposure/internet use and maternal health services\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeighted Prevalence (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of internet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.18\u0026ndash;16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of mobile phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.05\u0026ndash;59.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of laptop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.23\u0026ndash;10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.27\u0026ndash;62.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;4 ANC visits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.82\u0026ndash;60.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.00-83.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePNC within 48 hours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.38\u0026ndash;69.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eBivariate Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003ePatterns of antenatal care use by age\u003c/h2\u003e\n \u003cp\u003eFigure 1 shows that the percentage of women who made at least four ANC visits varies depending on their age group. Younger women (15 to 19 years old) and older women (45 to 49 years old) had lower ANC coverage, but the middle-aged groups (25 to 34 years old) had slightly higher percentages of women who went to four or more ANC visits\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eAssociations between media and internet exposure and the use of maternal healthcare\u003c/h2\u003e\n \u003cp\u003eWomen with internet exposure had a higher prevalence of facility delivery, 98.5% (Rao-Scott \u0026chi;\u0026sup2;=57.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, media exposure was associated with a higher prevalence of facility-delivery (87.2%, Rao-Scott \u0026chi;\u0026sup2;=47.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWomen with internet access were more likely to have attended at least four ANC visits (77.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and those exposed to media presented a similar positive trend (64.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eA proportion of (65.2%; p\u0026thinsp;=\u0026thinsp;0.76) of Internet users attended PNC within 48h, while that, among women with media exposure was (64.6%; p\u0026thinsp;=\u0026thinsp;0.078) (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003ePatterns of antenatal care utilization across wealth quintiles and internet use\u003c/h2\u003e\n \u003cp\u003eFigure 2 presents a heatmap showing the proportion of women who attended at least four ANC visits, across different wealth quintiles and levels of internet use. Among women without internet access, ANC attendance was highest in the poorest quintile and progressively increased with wealth, reaching the highest levels in the richest quintile. On the other hand, ANC coverage among internet users seemed to be fairly constant across all income levels, exhibiting moderate-to-high utilisation irrespective of socioeconomic background. Overall, the results indicate that wealth disparities in the use of prenatal care services are more pronounced among women without internet access, while internet use can help mitigate some of these inequalities by promoting more even participation in prenatal care services across all economic strata.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eMultivariate analysis\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of wealth status and internet use on facility delivery\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure 3 shows how facility delivery is shaped by the interaction of wealth quintile and internet use. In the poorest quintile (Q1) of women, those without internet access gave birth in healthcare facilities at a significantly higher rate (100%) compared to those with internet access (51.7%). In contrast, within the second quintile (Q2), internet users were more likely to deliver in a facility (66.9%) than those without internet access (49.1%). For women in the middle to high income quintiles (Q3 to Q5), the rate of hospital births stayed high (over 88%) in both groups, with only small differences between internet users and non-users.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003ePredictors of adequate antenatal care (ANC\u0026thinsp;\u0026ge;\u0026thinsp;4 Visits)\u003c/h2\u003e\n \u003cp\u003eThe regression results presented in the table indicate that mobile phone usage serves as a robust and significant predictor of sufficient antenatal care visits (AOR\u0026thinsp;=\u0026thinsp;1.889; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Media exposure showed a marginally significant result (AOR\u0026thinsp;=\u0026thinsp;1.332; p\u0026thinsp;=\u0026thinsp;0.0505). Women aged 25\u0026ndash;29 and 30\u0026ndash;34 were significantly more likely to achieve adequate ANC visits compared to those aged 15\u0026ndash;19. Other age groups did not show significant differences. Education and wealth status did not show significant effects after adjustment. Urban residence and higher parity were also not significant predictors. Regional variation was a predictor. Overall, as shown in Table\u0026nbsp;5 access to mobile phones, age, and regional variation are key predictors to have an adequate antenatal care. Socioeconomic status had less impact after adjustment (Table\u0026nbsp;5).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003ePredictors of Facility Delivery\u003c/h2\u003e\n \u003cp\u003eWhen we controlled potential confounders, many factors showed significant associations with facility delivery. Women who owned a mobile phone had higher odds of delivering in a health facility (AOR\u0026thinsp;=\u0026thinsp;1.58; p\u0026thinsp;=\u0026thinsp;0.016). Likewise, urban women were more than three times as likely to deliver in a facility as those residing in rural areas. Education and wealth index became significant predictors for facility delivery. Although parity showed a marginal negative association (AOR\u0026thinsp;=\u0026thinsp;0.58; p\u0026thinsp;=\u0026thinsp;0.051), Internet use, media exposure, and regional variation were not associated with facility delivery after we did the adjustment did the adjustment (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;5).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictors of Postnatal Care (PNC within 48 Hours)\u003c/h2\u003e\n \u003cp\u003eAs shown in Table 5 We found important predictors of PNC within 48 hours. Women exposed to media were hardly going for timely PNC compared to those who were not exposed (AOR\u0026thinsp;=\u0026thinsp;0.72; p\u0026thinsp;=\u0026thinsp;0.026). We found an inverse association about Education and parity too. Other factors, including internet use, mobile phone ownership, wealth, residence, and age, were not significantly associated with early PNC. The results revealed an unusual trend. Media exposure, higher education, and higher parity were associated with lower odds of receiving postnatal care within 48 hours.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted Odds Ratios (aOR) for Antenatal Care (ANC\u0026thinsp;\u0026ge;\u0026thinsp;4), Facility Delivery, and Postnatal Care (PNC within two days)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eANC\u0026thinsp;\u0026ge;\u0026thinsp;4 visits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFacility delivery\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePNC within 2 days\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternet use (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.757\u0026ndash;2.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u0026ndash;9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u0026ndash;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u0026ndash;1.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u0026ndash;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u0026ndash;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMobile phone use (Yes vs No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.444\u0026ndash;2.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u0026ndash;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u0026ndash;1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge 20\u0026ndash;24 vs 15\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.694\u0026ndash;2.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u0026ndash;1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u0026ndash;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge 25\u0026ndash;29 vs 15\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.046\u0026ndash;3.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u0026ndash;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u0026ndash;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge 30\u0026ndash;34 vs 15\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.065\u0026ndash;3.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u0026ndash;2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u0026ndash;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge 35\u0026ndash;39 vs 15\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.783\u0026ndash;3.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u0026ndash;2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u0026ndash;2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge 40\u0026ndash;44 vs 15\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u0026ndash;3.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u0026ndash;3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u0026ndash;4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge 45\u0026ndash;49 vs 15\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u0026ndash;3.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u0026ndash;3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u0026ndash;4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary education vs none\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.769\u0026ndash;1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20\u0026ndash;2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u0026ndash;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary\u0026thinsp;+\u0026thinsp;education vs none\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.896\u0026ndash;1.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.17\u0026ndash;7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u0026ndash;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth: Poorer vs middle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.489\u0026ndash;1.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u0026ndash;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u0026ndash;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth: Poorest vs middle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.541\u0026ndash;1.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u0026ndash;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u0026ndash;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth: Richer vs middle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.587\u0026ndash;1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u0026ndash;5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u0026ndash;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth: Richest vs middle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736\u0026ndash;1.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u0026ndash;7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u0026ndash;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban vs rural residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.495\u0026ndash;1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34\u0026ndash;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u0026ndash;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u0026thinsp;\u0026ge;\u0026thinsp;3 vs 1\u0026ndash;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.596\u0026ndash;1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u0026ndash;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.018\u0026ndash;1.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u0026ndash;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eTo examine the robustness of the findings, we have performed a sensitivity analysis. We substituted laptop use (MT4) for internet use (MT9) in the regression model. Our model remained statistically significant (Likelihood Ratio test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The model fit indices (AIC\u0026thinsp;=\u0026thinsp;2509.5, c-statistic\u0026thinsp;=\u0026thinsp;0.637) were comparable to the primary analysis. In this specification, laptop use was not significant nor associated with the likelihood of completing at least four ANC visits (AOR\u0026thinsp;=\u0026thinsp;1.31, p\u0026thinsp;=\u0026thinsp;0.343). However, other key predictors retained statistical significance. They were consistent with the main model.\u003c/p\u003e\n\u003cp\u003eIn Summary, the sensitivity analysis confirmed the robustness of the main findings. We found that laptop use itself was not a significant predictor. But the associations of media exposure, maternal age, and higher education with adequate ANC utilization remained stable across model specifications (Supplementary Table S3).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study, which involved Togolese women, investigated the relationship between maternal health care utilization behaviours (ANC\u0026thinsp;\u0026ge;\u0026thinsp;4 visits, facility delivery, and PNC within 48 hours) and media exposure, internet and mobile phone use, and sociodemographic characteristics. Several interesting patterns emerged, which both align with and diverge from prior work. Below, we discuss key findings, possible explanations, policy implications, limitations, and directions for future research.\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eMobile phones stood out, predicted\u0026thinsp;\u0026ge;\u0026thinsp;4 ANC visits (AOR\u0026thinsp;=\u0026thinsp;1.889) and facility delivery (AOR\u0026thinsp;=\u0026thinsp;1.580). Media exposure had a small, borderline positive link with ANC (AOR\u0026thinsp;=\u0026thinsp;1.332, p\u0026thinsp;=\u0026thinsp;0.0505). Unexpectedly, media exposure was linked with lower odds of PNC within 48 hours (AOR\u0026thinsp;=\u0026thinsp;0.716). Internet use looked very strong in crude comparisons but lost significance after we adjusted for sociodemographic factors. These numbers help frame the rest of the discussion.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eDigital access and maternal health service utilization\u003c/h2\u003e \u003cp\u003eMobile phone use was a consistent predictor of maternal health service utilization across our models. Similar benefits have been reported in several low-resource settings, where mobile- and SMS-based interventions improved antenatal care attendance and skilled delivery, including Ghana\u0026rsquo;s T4MCH program [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Evidence from digital health reviews suggests that simple tools such as SMS and voice calls are often more effective than complex platforms in contexts with limited connectivity and digital literacy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which may explain why mobile phone use remained significant while laptop ownership or general internet access did not.\u003c/p\u003e \u003cp\u003eMobile phone use, however, was not associated with early postnatal care. The immediate postpartum period is time-sensitive and often constrained by transport needs, household responsibilities, and health system capacity. While mobile phones can support communication, they are unlikely to overcome these physical and organizational barriers on their own [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInternet use showed strong associations with facility delivery in unadjusted analyses, but these associations weakened after accounting for education, wealth, and residence, suggesting confounding by socioeconomic advantage. Heatmap patterns nonetheless indicated that antenatal care coverage among internet users was relatively high across all wealth groups, whereas among non-users it increased sharply with wealth. This suggests that internet access may act as a facilitating resource for ANC utilization rather than an independent driver [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consistent with this, internet-based interventions often require higher literacy and stable connectivity, limiting their effectiveness in low-resource settings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, interaction analyses showed no evidence that the association between digital access and antenatal care differed by place of residence. This implies that once basic access is available, mobile-based digital content may offer similar potential benefits in rural and urban settings. Although urban\u0026ndash;rural disparities in digital health uptake have been reported elsewhere [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], our findings suggest that patterns of use may matter more than geographic location alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eMedia exposure and mixed associations\u003c/h2\u003e \u003cp\u003eMedia exposure showed a borderline positive association with ANC (aOR\u0026thinsp;=\u0026thinsp;1.33, p\u0026thinsp;=\u0026thinsp;0.0505) but was not predictive of facility delivery after adjustment, and interestingly, was negatively associated with PNC within 48 hours (aOR\u0026thinsp;=\u0026thinsp;0.72). The negative PNC association is unexpected and contrasts with much of the literature that finds a positive role of media in maternal healthcare [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany studies in sub-Saharan Africa and beyond show that exposure to radio, television, newspapers is positively associated with maternal care use (ANC, institutional delivery, PNC) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In Nepal, Sharma et al. (2024) also found positive associations of media exposure with ANC, facility delivery, and PNC utilization [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research indicates that numerous pregnant women increasingly depend on phones, social media, and online communities for information [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. this may influence their assessment of necessary services and timing.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eEducation, wealth, and urbanization on facility delivery\u003c/h3\u003e\n\u003cp\u003e Our results for facility delivery followed a familiar pattern. Education played a major role. Women who had only gone to primary school were more likely to give birth in a health facility (AOR\u0026thinsp;=\u0026thinsp;1.79), and women who had gone to secondary school or higher were even more likely to do so (AOR\u0026thinsp;=\u0026thinsp;3.97). This is consistent with what Bobo et al. found. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Wealth created a clear divide as well. Women in the poorest groups were much less likely to deliver in a facility (AOR\u0026thinsp;=\u0026thinsp;0.47 and 0.31), while richer women were more likely to do so (AOR\u0026thinsp;=\u0026thinsp;2.52). This reflects what many studies in Africa have already shown [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Living in an urban area also made a difference (AOR\u0026thinsp;=\u0026thinsp;3.34), likely because of shorter distances and better facility availability. Digital tools helped, but they did not remove these structural inequalities.\u003c/p\u003e\n\u003ch3\u003eAge, parity, and regional variation\u003c/h3\u003e\n\u003cp\u003eAge and region also shaped service use. Women in their late twenties and early thirties were more likely to reach ANC4+, while adolescents were less likely to do so. It is possibly due to stigma and limited autonomy. Regional differences suggest that some areas have better outreach or easier physical access to services. Parity played a role too. Women with several children were less likely to return for early PNC and showed slightly weaker patterns for facility delivery. They may rely on past experience or simply face more practical constraints at home.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eRobustness, sensitivity, and interaction analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses using laptop ownership as an alternative indicator of digital access produced results consistent with the main models. Laptop use was not independently associated with adequate ANC utilization, while associations for mobile phone use, maternal age, education, and media exposure remained stable. Interaction analyses showed that neither internet use nor place of residence independently or jointly predicted ANC utilization, suggesting that access alone may be insufficient and that how women engage with digital tools is likely more important.\u003c/p\u003e \u003cp\u003eThese findings are consistent with the broader digital health literature, which indicates that although web-based platforms, apps, and social media can support maternal health, their impact is often limited by gaps in digital literacy, connectivity, and health system context [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In low-resource settings, mobile phones, requiring lower bandwidth and offering greater accessibility, tend to be more effective for health communication than laptops or general internet access [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Studies also show that pregnant women commonly rely on mobile-based platforms and social media for information and support [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Overall, our results reinforce the idea that strengthening mobile-based engagement may be more impactful for improving ANC uptake than expanding internet access alone.\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eContinuum of maternal care\u003c/h2\u003e \u003cp\u003eOur results echo a known pattern of dropout along the continuum of care: many women initiate ANC and deliver at facility, but fewer access early PNC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In their study across 25 SSA countries, only 30% of women received the full recommended package (ANC4+, skilled birth, PNC). The fact that our PNC determinants differ from those of ANC and facility delivery underscores how PNC may face unique barriers (timing constraints, postpartum mobility, health system bottlenecks). Global analyses have similarly highlighted persistent gaps in postnatal care coverage, particularly within the first two days after birth, due to system-level and logistical constraints [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths. It uses nationally representative MICS data, applies appropriate survey weighting and design-based analyses, and incorporates a comprehensive set of sociodemographic covariates. The focus on digital access and media exposure provides a contemporary perspective on maternal health-seeking behaviors in Togo, a context where such factors have been little studied.\u003c/p\u003e \u003cp\u003eHowever, we acknowledge several limitations. The cross-sectional design precludes causal inference. All measures are self-reported and subject to recall error, particularly for the timing of early postnatal care. We did not distinguish between different media channels. Additionally, important factors such as distance to facilities, quality of care, partner influence, and facility readiness were not available in the dataset. Finally, the findings reflect the context of Togo in 2017, although these data remain the most recent nationally representative evidence available.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003eRecommendations and Programmatic Implications\u003c/h2\u003e \u003cp\u003e \u003cb\u003eIntroduce a lightweight digital follow up system (mMaternalCARE)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur findings suggest that many gaps in ANC and early PNC, and even children immunization, come from simple follow-up challenges. Health workers often search paper registers or make manual calls to track missed visits, which is slow and easy to miss. A small, mobile-first digital system, something like Maternal Care could help. The idea is straightforward: automate reminders, flag mothers who miss ANC, PNC, or immunization appointments, and give health workers a clear dashboard so they don\u0026rsquo;t have to chase lists by hand. The system would rely on SMS and USSD (not full internet) so it works even for women with basic phones. It could also help CHWs prioritize follow-up using factors that came out strongly in our results (parity, wealth, education, region, mobile ownership). This type of tool would reduce missed visits, lighten the workload, and make outreach more systematic, especially in rural and low-resource areas. It also aligns with growing regional efforts to integrate client-tracking systems with DHIS2 and other national platforms. Evidence from mHealth literature confirms that mobile technology can improve maternal health outcomes when well implemented [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn addition to this, improving early postnatal care will likely require targeted health system interventions, including improved postpartum follow-up protocols, extended service availability, and transport support. Media strategies should be reviewed to ensure that messaging around postnatal care is clear, actionable, and supportive. Rural health facility quality must be improved, and also expand infrastructure. These are essential for equitable maternal care.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFuture research should disaggregate media exposure by channel and intensity, and explore longitudinal or intervention designs to assess causality. Integrating geospatial measures of access and facility capacity could further clarify how digital and structural factors interact to shape maternal health service utilization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study explored maternal health service utilization in Togo and the role of digital access, media exposure, and sociodemographic factors in shaping care-seeking behaviors. Mobile phone use emerged as a consistent facilitator of antenatal care attendance and facility delivery, highlighting the value of simple digital tools in resource-constrained health systems.\u003c/p\u003e \u003cp\u003eAt the same time, the findings underscore important limits. Media exposure was not associated with improved early postnatal care and, in fact, was linked to lower uptake, suggesting that information alone is insufficient. Internet use showed strong crude associations but was largely explained by underlying socioeconomic advantage after adjustment. Across outcomes, structural factors particularly education, wealth, and urban residence remained central determinants of service utilization, while early postnatal care continued to be the weakest point in the maternal care continuum.\u003c/p\u003e \u003cp\u003eOverall, these results suggest that digital tools can support maternal health service use, but their impact depends on context. Mobile-based approaches are most effective when combined with accessible services, adequate infrastructure, and supportive social environments. Strengthening mobile engagement, improving postnatal care accessibility, and targeting adolescents and women with lower education levels may help ensure that more women complete the full continuum of maternal care in Togo.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the School of Public Health, SRM Institute of Science and Technology (SRM IST), for their guidance and support throughout the study and manuscript submission. We extend our appreciation to the United Nations Children\u0026apos;s Fund (UNICEF) team and TOGO UNICEF for providing access to the publicly available datasets utilized in this research. We also thank our colleagues and mentors for their valuable feedback and encouragement, which greatly enhanced the quality of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYK conceptualized the study, designed the methodology, conducted data analysis, interpretation, and manuscript drafting. ST, KSG, SAA, and RST contributed to data analysis and manuscript drafting. Dr GJH offered methodological guidance and substantive revisions to the manuscript. All authors reviewed and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study are publicly available through the UNICEF MICS program. Specifically, this study used data from the Togo 2017 Multiple Indicator Cluster Survey (MICS-6). Access to the dataset can be obtained upon approval of a data request submitted through the UNICEF MICS website (https://mics.unicef.org/surveys). All analyses were conducted using anonymized secondary data in accordance with ethical research standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was not required, as it involved secondary analysis of publicly available, anonymized data from the United Nations Child\u0026rsquo;s Fund (UNICEF) Multiple Indicator Cluster Survey (MICS-5). The Togo MICS obtained ethical approval from institutional review boards (IRBs) and informed consent from all participants at the time of data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript in its current form.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLee BX, Kjaerulf F, Turner S, et al. Transforming Our World: Implementing the 2030 Agenda Through Sustainable Development Goal Indicators. 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Impacts of internet use on health inequality: a cross-country study. Front Public Health. 2022;10:935608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO recommendations on postnatal care of the mother and newborn. Geneva: WHO; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNICEF, World Health Organization. Postnatal care for mothers and newborns: highlights from the World Health Statistics. New York: UNICEF; 2022.\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Maternal health, facility delivery, digital access, media exposure, socio-economic inequality, Togo, MICS","lastPublishedDoi":"10.21203/rs.3.rs-8635479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8635479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMaternal mortality continues to cause a significant public health challenge in Togo, where inequalities in access, continue to reflect both socioeconomic and digital divides. With the rapid expansion of mobile and internet technologies, understanding how these factors interact with traditional determinants such as education, wealth, and residence is a crucial need for designing and implementing health interventions for all. Hence, this study aimed to analyze the influence of digital access (media exposure, internet use, and mobile phone use) after controlling socioeconomic and demographic characteristics, on maternal health-seeking behaviors such as Antenatal Care (ANC\u0026thinsp;\u0026ge;\u0026thinsp;4 visits), facility delivery, and Post Natal Care (PNC) within 48 hours.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study analyzed Togo\u0026rsquo;s latest Multiple Indicator Cluster Survey (MICS) data available (2017). A total of 7,326 weighted observations were analyzed using survey-weighted binary logistic regression to identify predictors of maternal health service utilization. An interaction analysis and sensitivity analysis were performed to evaluate the variation of significance in place of residence as well as the robustness findings. All models accounted for the complex sampling design and adjusted for potential confounders.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, 57.1% of women had four or more ANC visits, 79.6% delivered in a health facility, and 66.7% received PNC within 48 hours. After adjustment, mobile phone use was strongly associated with adequate ANC utilization (AOR\u0026thinsp;=\u0026thinsp;1.89; 95% CI: 1.44\u0026ndash;2.47; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For facility delivery, women with primary [Adjusted Odds Ratio (AOR)\u0026thinsp;=\u0026thinsp;1.79; 95% CI: 1.20\u0026ndash;2.67; p\u0026thinsp;=\u0026thinsp;0.0046] and secondary or higher education (AOR\u0026thinsp;=\u0026thinsp;3.97; 95% CI: 2.17\u0026ndash;7.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significantly higher odds compared to those with no education. Urban women (AOR\u0026thinsp;=\u0026thinsp;3.34; 95% CI: 1.34\u0026ndash;8.34; p\u0026thinsp;=\u0026thinsp;0.010), those in richer quintiles were more likely to deliver in facilities. For PNC, media exposure (AOR\u0026thinsp;=\u0026thinsp;0.72; 95% CI: 0.53\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.026), higher parity (AOR\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.44\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.044), and lower education levels were associated with lower PNC attendance within 48 hours. Sensitivity and interaction analyses confirmed the robustness of the main findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDigital access influences, but does not replace, the impact of education, wealth, and residence on maternal health-seeking behaviors. Expanding mobile connectivity and targeted digital literacy, especially among poorer and rural women, could strengthen equitable access to maternal health services across Togo.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrial Registration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"The Interplay of Digital Access and Socioeconomic Inequality in Maternal Health Service Utilization in Togo: Evidence from the Multiple Indicator Cluster Survey using Survey-Weighted Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 16:03:48","doi":"10.21203/rs.3.rs-8635479/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-31T12:59:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T23:22:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338228890982469556463606280187513273945","date":"2026-03-12T08:29:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T08:40:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146382995732637951376449588427888918688","date":"2026-03-10T08:33:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228422725149794481798457194329207625049","date":"2026-03-10T07:26:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T14:33:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160512184213891181392079293010336201117","date":"2026-03-03T09:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T15:52:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-23T08:47:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T13:37:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T13:32:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-19T05:42:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9d390f7-2862-49ae-acdf-dcb32dc61c5a","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T16:03:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 16:03:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8635479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8635479","identity":"rs-8635479","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}