Unveiling the Sociodemographic and Socioeconomic Determinants of Antenatal Care Utilization in Bangladesh: Insights from the 2017-18 BDHS

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Mojammel Haque Sakib, Muhammad Khairul Alam, Mst. Nilufar Yasmin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4730450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 May, 2025 Read the published version in Journal of Health, Population and Nutrition → Version 1 posted 10 You are reading this latest preprint version Abstract Background Antenatal care (ANC) is indispensable for supervising and enhancing the health of both the mother and the baby during pregnancy. It helps to reduce the risks of complications and ensures better pregnancy outcomes. This study investigates the aspects that influence antenatal care (ANC) visits in Bangladesh, focusing on sociodemographic and socioeconomic factors. Methods The study used the most current, nationally representative data from the 2017–18 Bangladesh Demographic and Health Survey (BDHS). Mann-Whitney and Kruskal-Wallis tests were conducted for bivariate analysis. The Boruta algorithm was utilized for variable selection. After employing various regression models, including Poisson Regression (PR), Negative Binomial Regression (NBR), and Multiple Linear Regression (MLR), we evaluated their performance and selected Negative Binomial Regression for parameter estimation and interpretation. Results Our results reveal that less than 50% of women meet the WHO-recommended minimum number of ANC visits. Women with secondary and higher education (IRR 1.42 & 1.46, 95% CI 1.28–1.56 & 1.31–1.64), Rich wealth status (IRR 1.13, 95% CI 1.07–1.19), Cesarian section (IRR 1.28, 95% CI 1.23–1.34), media coverage (IRR 1.20, 95% CI 1.14–1.25) were more likely to have frequent ANC visits. Conversely, women with higher birth order (IRR 0.94 & 0.82, 95% CI 0.89–0.99 & 0.75–0.91), unintentional pregnancy (IRR 0.92 & 0.85, 95% CI 0.87–0.97 & 0.79–0.92) were less likely to have ANC vists. Conclusion Given that the majority of women in Bangladesh do not receive adequate antenatal care, achieving national and international maternal and child health goals will be challenging. This study identified factors hindering access to high-quality prenatal care, which the Bangladeshi administration should address through focused actions. Antenatal Care Visit Maternal Health Bangladesh Demographic and Health Survey Negative Binomial Regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Maintaining the quality of antenatal care (ANC) is critical to elevating the support provided to women throughout their pregnancy, thereby safeguarding the well-being of both the mother and the baby.[ 1 ] High-quality ANC impacts pregnancy from both clinical and psychological perspectives, as it helps women prepare for childbirth and the responsibilities of motherhood[ 2 ]. Annually, an estimated 300,000 women succumb to complications stemming from pregnancy or childbirth worldwide, which is around 800 each day on average.[ 3 ] Annually, the South Asian region alone is responsible for over one-third of all maternal and child fatalities worldwide[ 4 ]. The most recent Sustainable Development Goals prioritize declining of global maternal mortality rates to 70 deaths per 100,000 live births and the neonatal mortality rate to 12 deaths per 1000 live births by the year 2030[ 5 , 6 ]. High-quality ANC protects against unexpected pregnancy outcomes. Although 62% of pregnant women globally participated in a minimum of four World Health Organization (WHO)-recommended antenatal care (ANC) activities in 2017, the comparable proportion for Bangladesh stood at only 47%[ 1 ]. Widespread access to high-quality prenatal care (ANC) can play a significant role in helping to achieve national and international goals for mother and child health, such as lowering the rate of maternal and newborn mortality [ 7 ]. The Focused Antenatal Care (FANC) Model that was suggested by the World Health Organization (WHO) in the past, it is advised that when a pregnant woman is under typical conditions, she should have at least four ANC visits [ 8 ]. The 2016 WHO ANC Model incorporates eight ANC interactions to help accomplish the Sustainable Development Goals (SDGs) to reduce mother and child mortality[ 9 ]. However, Bangladesh still promotes four ANC visits according to the older WHO guidelines[ 8 ]. Inequities in using maternal health services are prevalent among developing countries[ 10 , 11 ]. Maternal deaths due to inadequate healthcare utilisation are more common among less-educated, poor, and rural populations[ 10 , 12 ]. There is a stark disparity in South Asia, with a 33 percentage-point difference between urban and rural areas in the coverage of births attended by skilled health personnel[ 13 ]. The development agenda now prioritizes addressing these enduring disparities, highlighting the necessity of fair access to basic services for all demographic groups. Equitable access is becoming a more important factor in assessing the success of health systems, which is in line with gradually reaching universal health coverage (UHC) with an emphasis on equity[ 14 ]. The Bangladesh Demographic and Health Survey (BDHS) reveals increased ANC visits from any healthcare facility. ANC coverage rose by 31% from 2004 to 2017 [ 15 – 17 ]. Bangladesh continues to be one of the top ten nations in the world for maternal mortality, accounting for approximately 60% of all maternal deaths worldwide, even with this increase[ 18 ]. Many studies have looked into the factors that affect ANC visits and postpartum care in Bangladesh[ 1 , 8 , 11 , 19 , 20 ]. The majority of them emphasized the importance of women's education and their wealth status in receiving high-quality prenatal care, as well as the differences in prenatal care by area[ 1 , 8 , 19 , 21 , 22 ]. Several studies have revealed that decision-making authority over one's own health care and media access has a significant influence as well[ 19 , 21 ]. Exposure to the media has also been found to have an impact on receiving prenatal treatment[ 11 , 20 , 23 ]. The Bangladeshi government is dedicated to accomplishing the Sustainable Development Goals (SDG) as it has presented two Voluntary National Review (VNR) so far[ 24 ]. Over recent decades, socioeconomic disparities and their ramifications on population health, particularly maternal and child health, have emerged as pivotal subjects of concern[ 25 ]. To provide policymakers with assistance in reducing the occurrence of maternal and infant mortality, it is of the utmost importance to identify the socioeconomic and socio-demographic factors that have an impact on maternal health. With the assistance of a count regression model, the general goal of our study is to ascertain the degree to which socioeconomic and demographic factors impact the total number of prenatal care visits. To the best of our knowledge, count regression models such as Poisson Regression (PR) and Negative Binomial Regression (NBR) were not utilized in the process of modeling the number of prenatal visits using data from the BDHS (2017-18). So, this investigation will thereby increase the range of evidence in this field. Materials and Methods Study Setting The data for this study was extracted from the Bangladesh Demographic and Health Survey, specifically the latest available dataset (BDHS 2017–2018). The entire population of Bangladesh living in non-institutional housing was covered by the nationally representative Bangladesh Demographic and Health Survey (BDHS) for the 2017-18 fiscal year. The 2011 Bangladesh Bureau of Statistics (BBS) Population and Housing Census provided the sample frame for the study. An enumeration area (EA) is defined as a group of about 120 houses that make up the principal sampling unit (PSU). Bangladesh is divided into eight administrative divisions, which allow the country as a whole to be separated into urban and rural areas[ 16 , 26 ]. . A two-stage stratified sampling technique was used by the BDHS. According to ICF specifications and BBS implementation, 675 EAs (250 urban and 425 rural) were chosen in the first stage based on a probability proportionate to their size. In order to provide a sample frame for the second step, a thorough household listing was subsequently carried out within these EAs. In the second phase, a total of 20,250 households were carefully chosen in order to produce accurate estimates of population and health for the entire nation, as well as for each division and urban and rural areas separately. Approximately 20,127 married women between the ages of 15 and 49 were interviewed for the survey[ 26 ]. Three clusters were disqualified because of flooding, even though 672 clusters had a successful implementation. There were two rural clusters in Rajshahi and Rangpur and one urban cluster in Dhaka. In the end, the poll covered 20,160 households. Sampling weights were used in the analysis to make sure the data appropriately reflects the national and divisional levels of government. The integrity of the survey results is preserved by these weights, which are intended to reduce any notable variations in survey indicators. For our analysis, 4920 observations were retained after removing the ineligible ones. Outcome variable The study's outcome variable was the number of antenatal care visits, representing how frequently expectant mothers received expert medical attention during their pregnancy. This variable provides insights into the quality and accessibility of prenatal care across the population, making it a critical indicator of a mother's health and access to healthcare services[ 2 , 19 ]. Researchers can find trends and differences in healthcare access between various demographic groups by examining the number of visits, which can assist in guiding public health initiatives. Explanatory Variables The explanatory variables in the study were categorized into socio-demographic, socio-economic, and contextual factors to provide a structured analysis of the determinants of antenatal care visits. Socio-demographic variables included the respondent’s age group (≤ 20, 21–30, ≥ 31), place of residence (urban, rural), number of household members (≤ 5, 6–10, > 10), birth order (1, 2–3, 4+), age at first birth ( 25), birth in the last three years (1, > 1), sex of the household head (male, female), and religion (Islam, Hinduism, Christianity, Buddhism). Socio-economic variables encompassed the highest level of education (no education, primary, secondary, higher), wealth index (poor, middle, rich), partner’s education level (no education, primary, secondary, higher), husband’s occupation (unemployed, agricultural or household work, service and sales, others), employment status (yes, no), and health insurance (yes, no). Contextual factors included the division (Dhaka, Chittagong, Khulna, Rajshahi, Mymensingh, Rangpur, Sylhet, Barisal), whether the pregnancy was wanted at that time (yes, no), cesarean section at previous birth (yes, no), and coverage of media (yes, no). A strong framework for examining the factors influencing the use of prenatal care in Bangladesh was provided by these variables. Statistical Analysis First, the distribution of the number of antenatal care visits was analyzed by a bar plot. A bivariate analysis was performed using either the Mann-Whiteney or Kruskall-Wallis test to ascertain the median number of visits for each category of covariates. The Kruskal-Wallis and Mann-Whitney tests are non-parametric methods for evaluating median differences between groups, with the former suitable for more than two groups and the latter for two groups[ 27 ]. The Boruta Algorithm, a machine learning feature selection method, was applied to choose covariates for modeling the data. This technique utilizes a random forest approach to determine feature importance by comparing actual features' accuracy loss to randomly shuffled shadow features, thus identifying critical attributes amidst random variations.[ 28 ]. After that, using the Akaike Information Criterion (AIC) and log-likelihood, we assessed three regression models such as Poisson regression (PR), Negative binomial regression (NBR), and Multiple linear regression (MLR). The Akaike information criterion is a mathematical framework used for model selection and parsimony assessment in model construction.[ 29 ] Since the negative binomial regression model had a lower AIC value, we finally used it for parameter estimation and interpretation. A flowchart (Fig. 1 ) was used to illustrate the entire study. Stata 17 and R 4.3.1 were the statistical tools used during the analysis. Results and Analysis Descriptive Analysis Figure 2 displays the distribution of antenatal care visits, revealing that over 50% of women do not receive the WHO-recommended minimum of at least four. This discrepancy is a significant concern that warrants attention. The distribution is positively skewed here. Table 1 Percentage distribution of the respondents and median number of antenatal visits by subgroups Factors Category n (%) Median (p-value) Age group 30 833 (16.93%) 3 Division Barisal 524 (10.65%) 3 0.0001 Chittagong 814 (16.54%) 3 Dhaka 728 (14.80%) 4 Khulna 510 (10.37%) 4 Mymensingh 594 (12.07%) 3 Rajshahi 519 (10.55%) 4 Rangpur 550 (11.18%) 4 Sylhet 681 (13.84%) 3 Place of residence Urban 1,692 (34.39%) 4 0.0000 Rural 3,228 (65.61%) 3 Education level No education 304 (6.18%) 2 0.0001 Primary 1,364 (27.72%) 2 Secondary 2,358 (47.93%) 4 Higher 894 (18.17%) 5 Religion Islam 4,503 (91.52%) 3 0.0587 Hinduism 392 (7.97%) 4 Buddhism 17 (0.35%) 5 Christianity 8 (0.16%) 5.5 Number of household members 10 359 (7.30%) 4 Household head Male 4,337 (88.15%) 3 0.2355 Female 583 (11.85%) 3 Wealth index Poor 2,058 (41.83%) 2 0.0001 Middle 882 (17.93%) 3 Rich 1,980(40.24%) 4 Birth Order 1 1,864 (37.89%) 4 0.0001 2–3 2,458 (49.96%) 3 4+ 598 (12.15%) 2 Table 1 (continued) Factors Category n (%) Median p-value Age at first birth 25 200 (4.07%) 6 Birth in three years 1 4,635 (94.21%) 3 0.0008 > 1 285 (5.79%) 3 Pregnancy wanted then 3,876 (78.78%) 4 0.0001 later 642 (13.05%) 3 no more 402 (8.17%) 2 Cesarean section No 3,275 (66.57%) 3 0.0000 Yes 1,645 (33.43%) 5 Health Insurance No 4,911(99.82%) 3 0.0172 Yes 9 (0.18%) 5 Husband’s education No education 678 (13.78%) 2 0.0001 Primary 1,654 (33.62%) 3 Secondary 1,633 (33.19%) 4 Higher 955 (19.41%) 5 Employment Status No 3,081 (62.62%) 3 0.0412 Yes 1,839 (37.38%) 3 Media Coverage No 1,761 (35.79%) 2 0.001 Yes 3,159 (64.21%) 4 Husband’s Occupation Unemployed 38 (0.7%) 3 0.001 Agricultural or Household work 924 (18.7%) 3 Service and Sales 2,032 (41.3%) 4 Others 1,926 (39.1%) 3 Table 1 summarizes the percentage distribution of respondents and the median number of antenatal visits across various subgroups based on different factors. About 24.47% of respondents are aged 20 or younger, 58.60% are between 20 and 30, and 16.93% are older than 30. The median number of antenatal visits is the same (3 visits) across all age groups. The p-value (0.0019) indicates a significant difference in the median antenatal visits across different age groups. The respondents are distributed across various divisions. Dhaka, Khulna, Rajshahi, and Rangpur divisions have the highest median number of antenatal visits (4 visits), while the divisions of Barisal and Sylhet have the lowest median (3 visits). The p-value (0.0001) suggests a significant difference in the median number of antenatal visits across divisions. More respondents reside in rural areas (65.61%) compared to urban areas (34.39%). Urban residents have a higher median number of antenatal visits (4) than rural residents (3). Most respondents have secondary education (47.93%), followed by primary (27.72%) and higher education (18.17%). Respondents with higher education have a higher median number of antenatal visits (5 visits), followed by secondary (4 visits) and primary education (2 visits). Although insignificant (p = 0.0587), Muslims constitute the majority (91.52%) with a median of 3 visits, while other religions have lower representation and slightly higher median visits. There's no significant difference in visit frequency by gender of household head (p = 0.2355). This suggests that the gender of the household head may not significantly influence antenatal care utilization. Wealthier women had more visits (median = 4) compared to poorer ones (median = 2), and this difference is significant (p = 0.0001). For the Firstborns, women had the highest median visits (4), followed by 2–3 (3) and 4+ (2). Those aged 18–25 have the most visits (median = 4) compared to others. The vast majority of respondents (94.21%) reported giving birth once within three years. There's a significant difference in visit frequency based on pregnancy intention (p = 0.0001), with those intending to have a child later or no more children having fewer visits than those who wanted the pregnancy then. A significant proportion of respondents who underwent a cesarean section (33.43%) had more visits. Most husbands had primary or secondary education, and there's a considerable difference in visit frequency based on the husband's education level (p = 0.0001), with higher-educated husbands being associated with more antenatal care visits. Most respondents had media coverage (64.21%) with more visits. Variable Selection: The output of the Boruta Algorithm in Fig. 4 suggests that the number of births in the last three years, religion, sex of household head, and health insurance are deemed unimportant in predicting the number of antenatal care visits. However, variables like the husband’s occupation and respondent's employment status show some indication of importance but are not confirmed. The remaining variables are considered important predictors of the number of antenatal visits. These findings led us to build a model by focusing on the confirmed important variables while considering the tentative ones cautiously. Model Selection Table 2 Comparison of models based on Akaike’s information criteria (AIC) and log Likelihood Model AIC Log Likelihood Goodness of fit test (P value) PR 22708.2 -11322.099 0.000 NBR 22047.08 -10990.542 0.000 MLR 23221.653 -11565.827 0.000 Table 2 represents the results of model selection criteria for the three initially fitted models (PR, NBR, MLR). Among these models, it is observed that the Negative Binomial Regression (NBR) has the lowest AIC value. Therefore, the Negative Binomial Regression model was selected to examine the variables influencing changes in the frequency of prenatal care visits. Fitted Negative Binomial Regression Model Table 3 Incident Rate Ratios (IRR) of different subgroups based on Negative Binomial Regression Factors Category IRR 95% CI for IRR P- value Lower Upper Age group 30 1.129 1.036 1.230 0.005 Division Barisal(ref) Chittagong 0.915 0.847 0.987 0.022 Dhaka 1.032 0.956 1.114 0.409 Khulna 1.144 1.055 1.239 0.001 Mymensingh 1.119 1.034 1.211 0.005 Rajshahi 1.075 0.991 1.166 0.079 Rangpur 1.290 1.192 1.395 0 Sylhet 0.948 0.875 1.027 0.194 Place of residence Urban (ref) Rural 0.882 0.845 0.920 0 Education level No education (ref) Primary 1.271 1.151 1.402 0 Secondary 1.421 1.286 1.569 0 Higher 1.466 1.310 1.640 0 Number of household members 10 1.007 0.936 1.084 0.834 Wealth index Poor (ref) Middle 1.087 1.028 1.150 0.003 Rich 1.134 1.072 1.199 0 Birth Order 1(ref) 2–3 0.943 0.896 0.992 0.025 4+ 0.829 0.756 0.910 0 Age at first birth 25 0.959 0.865 1.063 0.426 Pregnancy wanted then(ref) later 0.922 0.871 0.976 0.005 no more 0.856 0.790 0.929 0 Cesarean section No(ref) Yes 1.287 1.235 1.342 0 Husband’s education No education(ref) Primary 1.030 0.964 1.101 0.367 Secondary 1.131 1.055 1.213 0.001 Higher 1.206 1.110 1.310 0 Media Coverage No (ref) Yes 1.203 1.149 1.259 0 ref: Reference category The significant demographic, socioeconomic, and pregnancy-related components of fitted negative binomial regression are displayed in Table 3 , along with the incidence rate ratios (IRR) for each. Age group, division, place of residence, education level, number of household members, wealth index, birth order, age at first birth, intention to conceive, cesarean section, partner’s education, and exposure to media were independently associated with the number of antenatal visits. Women between 20 and 30 had 8.5% higher antenatal visits than those under 20 (IRR 1.085, 95% CI 1.025–1.149). Compared to the Barisal division, women from Khulna, Mymensingh, and Rangpur received 14.4%, 11.9%, and 29% more visits, respectively. (IRR 1.14, 95% CI 1.055–1.239; IRR 1.119, 95% CI 1.034–1.211 and 1.29, 95% CI 1.192–1.395). Comparing women in rural and urban areas, the former had 12% fewer visits (IRR 0.88, 95% CI 0.84–0.92). Women with secondary and higher education had more than 40% antenatal visits compared to those without education (IRR 1.42,95% CI 1.28–1.56 and 1.46, CI 1.31–1.64). The number of household members does not significantly affect the incidence rate. Middle-class and wealthy women had 8% and 13% more visits than the poor. The incidence rate is 0.943 times lower for birth order of 2–3 and 0.830 times lower for birth order of 4 + than for birth order of 1 (IRR 0.94, 95% CI 0.89-.99 and .82, 95% CI 0.75–0.91). The age at first birth does not significantly affect the incidence rate. Women who wanted to wait to get conceived and didn't want to have more children had 15% and 8% fewer visits, respectively, than women who wanted to get conceived right away (0.85, 95% CI 0.79–0.92 and 0.92, 95% CI 0.87–0.97). Individuals with a cesarean section had 28% more visits than those without (IRR 1.28, 95% CI 1.23–1.34). Compared to those who are illiterate, those whose husbands have completed secondary or higher education had 13% and 20% more visits, respectively (IRR 1.13, 95% CI 1.05–1.21 and 1.20, 95% CI 1.11–1.31). Those in media coverage had 20% more visits than those without (1.20, 95% CI 1.14–1.25). Discussion This study has identified several sociodemographic and socioeconomic variables that are significantly associated with the frequency of antenatal care (ANC) visits in Bangladesh. The findings reveal that less than 50% of women meet the World Health Organization (WHO) recommended minimum of four ANC visits, which aligns with the results of Akter et al.[ 22 ]. This percentage is notably lower than in neighbouring countries, such as India (59.25%) and Nepal (69%), but similar to Pakistan[ 30 – 32 ]. A key finding of this study is the pronounced urban-rural disparity in ANC visits, which is clearly illustrated in Fig. 3 . Urban women were found to have 12% more visits than their rural counterparts. This disparity can be attributed to various factors, including better availability and accessibility of medical facilities, higher socioeconomic status, and greater educational attainment in urban areas[ 11 , 33 ]. Education, particularly for women, emerged as a critical determinant of ANC quality. Women with secondary or higher education levels were 42% and 46% more likely to attend ANC visits than those without or only primary education. Another study by Haque et al. also found that the frequency of ANC visits was 10.6% lower for mothers who did not continue their education after marriage[ 34 ]. Educated women are generally more informed about health issues and the benefits of medical care over traditional treatments[ 35 ]. The wealth status of women is found to be another significant determinant since middle-class and rich women had 8% and 13% more visits compared to poor, which is also found in several studies[ 1 , 23 , 36 ]. Poor women often cannot afford the costs associated with high-quality ANC, including consultation fees, diagnostic tests, medications, and transportation to healthcare facilities[ 37 ]. Birth order was also significantly related to the frequency of ANC visits. Mothers who already have children were less likely to have frequent ANC visits, which was also found in other studies[ 20 , 22 ] A study in Ethiopia revealed that higher birth order was inversely related to the timing of the first ANC visit, which led to fewer ANC visits[ 38 ]. The study discovered that a woman’s age affects how frequently she visits an ANC. Compared to younger mothers, older mothers were more likely to have frequent ANC visits. However, the age at first birth showed a different scenario. Women who gave first birth before 18 were more likely to have quality care than who gave later, though it was not highly significant. In addition, our study showed that women who desired a pregnancy later or who did not want more were less likely to visit frequently than those who did. This is also found in two studies by Biswas et al. and Islam et al.[ 11 , 21 ]. Unintended pregnancies are often linked to delayed initiation and insufficient use of antenatal care services[ 39 ]. Women with unintended pregnancies may be less prepared or less motivated to seek timely and regular prenatal care, leading to poorer maternal and child health outcomes Having a Caesarean section is another significant element in having more ANC visits. Women who had a Caesarean section had 28% more ANC visits than who did not. Healthcare providers typically recommend more frequent ANC visits for women with a history of Caesarean section to ensure any potential complications are detected and managed early[ 40 ]. The degree of education of a partner is equally important to ANC as the education of women. Women with highly educated partners tend to visit the ANC more frequently than those with less educated partners. Similar to a number of other studies, ours discovered media coverage to be a key influence[ 11 , 21 , 23 ]. Exposure to media increases awareness and knowledge about the importance of regular ANC visits, thereby encouraging more women to seek timely and comprehensive prenatal care[ 41 ]. Parallel to a study by Ali et al., we did not discover any significant association between the number of household members and the quality of ANC[ 23 ]. Strength and limitations The main advantage of this study is that it used data that is representative of the entire country. In addition, we have evaluated three distinct statistical models and determined which one is the most effective in terms of estimating parameters and, consequently, interpreting the data. While the Negative Binomial Regression Model was utilized for the final parameter estimation, we failed to consider the inflation of zeros in the response variable, which would have been more appropriate if a zero-inflated model had been employed. Conclusions This study highlights the significant impact of various sociodemographic and socioeconomic factors on antenatal care (ANC) visits in Bangladesh. Despite progress, the proportion of women meeting the WHO-recommended minimum ANC visits remains below 50%. Our findings indicate a notable urban-rural disparity, with rural women attending fewer ANC visits than their urban counterparts. Factors such as education level, wealth status, birth order, age, pregnancy intentions, and cesarean section history significantly influence ANC visit frequency. Women with higher education, better wealth status, and a history of cesarean sections are more likely to attend ANC visits. Conversely, higher birth order and unintended pregnancies reduce the likelihood of frequent ANC visits. These insights underscore the need for focused initiatives to address disparities and enhance ANC coverage, particularly among rural, less educated, and poorer women. By addressing these factors, policymakers can improve maternal health outcomes and work towards achieving national and international health goals. Abbreviations ANC Antenatal care BDHS Bangladesh Demographic and Health Survey WHO World Health Organization PR Poisson Regression NBR Negative Binomial Regression Model MLR Multiple Linear Regression Model IRR Incidence Rate Ratio SDGs Sustainable Development Goals AIC Akaike Information Criterion CI Confidence Interval Declarations Ethics approval and consent to participate: This study used publicly available data from the Bangladesh Demographic and Health Survey (BDHS) 2017. The survey was ethically approved by the Institutional Review Board of ICF International and the Ethics Review Committee of NIPORT. Consent for publication: Not applicable . Availability of data and materials: The dataset used in this article is sourced from the DHS Program database, which is accessible at https://dhsprogram.com/Data/ . The data sets utilized in the current analysis are available from the corresponding author upon reasonable request. For additional information, please contact the author in question. Competing interests: We declare that no known competing interests or personal relationships could have appeared to influence the work reported in this paper. Funding: We declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ Contributions: Md. Mojammel Haque Sakib: Conceptualization, Methodology, Data analysis, Writing - original draft Muhammad Khairul Alam: Conceptualization, Methodology, Data analysis, Writing - original draft. Mst. Nilufar Yasmin: Conceptualization, Writing - review & editing. Rumana Rois: Conceptualization, Methodology, Supervision, Writing - original draft. 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Sanogo NA, Fantaye AW, Yaya S. Universal Health Coverage and Facilitation of Equitable Access to Care in Africa. Front Public Health. 2019;7. NIPORT/Bangladesh NI of PR and, Associates/Bangladesh T- M and, Macro ORC. Bangladesh Demographic and Health Survey 2004. Dhaka, Bangladesh: NIPORT, Mitra and Associates, and ORC Macro; 2005. NIPORT/Bangladesh NI of PR and, Associates/Bangladesh T-. M and, International ICF. Bangladesh Demographic and Health Survey 2011. Dhaka, Bangladesh: NIPORT, Mitra and Associates, and ICF International; 2013. NIPORT/Bangladesh NI of PR and, Associates T-. M and, International ICF. Bangladesh Demographic and Health Survey 2014. Dhaka, Bangladesh: NIPORT, Mitra and Associates, and ICF International; 2016. Moinuddin M, Christou A, Hoque DME, Tahsina T, Salam SS, Billah SM, et al. Birth preparedness and complication readiness (BPCR) among pregnant women in hard-to-reach areas in Bangladesh. PLoS ONE. 2017;12:e0189365. Bhowmik KR, Das S, Islam MA. Modelling the number of antenatal care visits in Bangladesh to determine the risk factors for reduced antenatal care attendance. PLoS ONE. 2020;15. Chanda SK, Ahammed B, Hasan Howlader M, Ashikuzzaman M, Shovo TEA, Tanvir Hossain M. Factors associating different antenatal care contacts of women: A cross-sectional analysis of Bangladesh demographic and health survey 2014 data. PLoS ONE. 2020;15. Islam MM, Masud MS. Determinants of frequency and contents of antenatal care visits in Bangladesh: Assessing the extent of compliance with the WHO recommendations. PLoS ONE. 2018;13. Akter MB, Mahmud A, Karim MR. Determinants of Antenatal Care Visits in Bangladesh: A Quantile Regression Analysis. Health Serv Res Manag Epidemiol. 2023;10. Ali N, Sultana M, Sheikh N, Akram R, Mahumud RA, Asaduzzaman M, et al. Predictors of Optimal Antenatal Care Service Utilization Among Adolescents and Adult Women in Bangladesh. Health Serv Res Manag Epidemiol. 2018;5:233339281878172. Toward. 2030: Strategies for SDG success in Bangladesh | United Nations Development Programme. https://www.undp.org/bangladesh/news/toward-2030-strategies-sdg-success-bangladesh . Accessed 11 May 2024. Yiengprugsawan V, Lim LL, Carmichael GA, Sidorenko A, Sleigh AC. Measuring and decomposing inequity in self-reported morbidity and self-assessed health in Thailand. Int J Equity Health. 2007;6:23. NIPORT NI of PR and, Welfare T-, of H M. and F, F IC. Bangladesh Demographic and Health Survey 2017-18. Dhaka, Bangladesh: NIPORT/ICF; 2020. Zhang B, Zhang Y, Mann-Whitney U et al. test and Kruskal-Wallis test should be used for comparisons of differences in medians, not means: Comment on the article by van der Helm-van Mil. Arthritis Rheum. 2009;60:1565. Kursa MB, Rudnicki WR. Feature Selection with the Boruta Package. J Stat Softw. 2010;36:1–13. Bonakdari H, Zeynoddin M. Chapter 5 - Goodness-of-fit & precision criteria. In: Bonakdari H, Zeynoddin M, editors. Stochastic Modeling. Elsevier; 2022. pp. 187–264. Asim M, Hameed W, Saleem S. Do empowered women receive better quality antenatal care in Pakistan? An analysis of demographic and health survey data. PLoS ONE. 2022;17:e0262323. Bryce E, Katz J, Pema Lama T, Khatry SK, LeClerq SC, Munos M. Antenatal care processes in rural Southern Nepal: gaps in and quality of service provision—a cohort study. BMJ Open. 2021;11:e056392. Girotra S, Malik M, Roy S, Basu S. Utilization and determinants of adequate quality antenatal care services in India: evidence from the National Family Health Survey (NFHS-5) (2019-21). BMC Pregnancy Childbirth. 2023;23:800. Kibria GM, Al, Nayeem J. Association of rural-urban place of residence with adequate antenatal care visit in Bangladesh. PLOS Global Public Health. 2023;3:e0002528. Haque ME, Mallick TS, Bari W. Does dropout from school matter in taking antenatal care visits among women in Bangladesh? An application of marginalized poisson-poisson mixture model. BMC Pregnancy Childbirth. 2022;22:476. Bhowmik J, Biswas RK, Woldegiorgis M. Antenatal care and skilled birth attendance in Bangladesh are influenced by female education and family affordability: BDHS 2014. Public Health. 2019;170:113–21. Islam MA, Kabir MR, Talukder A. Triggering factors associated with the utilization of antenatal care visits in Bangladesh: An application of negative binomial regression model. Clin Epidemiol Glob Health. 2020;8:1297–301. Jo Y, Alland K, Ali H, Mehra S, Lefevre A, Pak S et al. Antenatal care in rural Bangladesh: current state of costs, content and recommendations for effective service delivery. BMC Health Serv Res. 2019;19. Woldeamanuel BT, Belachew TA. Timing of first antenatal care visits and number of items of antenatal care contents received and associated factors in Ethiopia: multilevel mixed effects analysis. Reprod Health. 2021;18:233. Dibaba Y, Fantahun M, Hindin MJ. The effects of pregnancy intention on the use of antenatal care services: systematic review and meta-analysis. Reprod Health. 2013;10:50. Fabbro MRC, Wernet M, Baraldi NG, de Castro Bussadori JC, Salim NR, Souto BGA, et al. Antenatal care as a risk factor for caesarean section: a case study in Brazil. BMC Pregnancy Childbirth. 2022;22:731. Fatema K, Lariscy JT. Mass media exposure and maternal healthcare utilization in South Asia. SSM Popul Health. 2020;11:100614. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 May, 2025 Read the published version in Journal of Health, Population and Nutrition → Version 1 posted Editorial decision: Revision requested 25 Dec, 2024 Reviews received at journal 23 Dec, 2024 Reviews received at journal 19 Dec, 2024 Reviewers agreed at journal 09 Dec, 2024 Reviewers agreed at journal 30 Nov, 2024 Reviewers agreed at journal 30 Nov, 2024 Reviewers invited by journal 30 Nov, 2024 Editor assigned by journal 25 Jul, 2024 Submission checks completed at journal 25 Jul, 2024 First submitted to journal 12 Jul, 2024 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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Mojammel Haque Sakib","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCTCZwGPA3gCkDSxI0cJzAKRFgngtQMUJCC5eoDu7+eHNHzVpMuaSz69u+FEgwcDf3p2AV4vZnWPG1jzHcngsZ+eU3ewBOkzizNkN+LXcSDCTZmCr4DG4nZN2gweoxUAil5CW9G+SP/4Btdw8k3bzD3FacswkeNtyeAxusB+7TaQtOcXWvH1pPJY9OWy3ZQwkeIjwS/rGmz++Jdubsx9/dvPNHxs5/vZe/FpAABoXPAZgkqByJC3sD4hSPQpGwSgYBSMPAAB8rkc9Mr3X0AAAAABJRU5ErkJggg==","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Mojammel Haque","lastName":"Sakib","suffix":""},{"id":335264775,"identity":"bc2fa6ce-4857-4f99-8966-dcd7c7dd0a9c","order_by":1,"name":"Muhammad Khairul Alam","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Khairul","lastName":"Alam","suffix":""},{"id":335264776,"identity":"d721d013-982c-4d28-8aae-d7b359cc98de","order_by":2,"name":"Mst. Nilufar Yasmin","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Mst.","middleName":"Nilufar","lastName":"Yasmin","suffix":""},{"id":335264777,"identity":"72142240-3383-40be-b631-ffbf95c1d10e","order_by":3,"name":"Rumana Rois","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Rumana","middleName":"","lastName":"Rois","suffix":""}],"badges":[],"createdAt":"2024-07-12 12:59:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4730450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4730450/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41043-025-00839-w","type":"published","date":"2025-05-16T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63370184,"identity":"05b94b64-a745-42e8-94cc-f0287aa2b7bc","added_by":"auto","created_at":"2024-08-27 11:48:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":514640,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart that outlines the comprehensive methodology employed in the study\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4730450/v1/b91a3fa99bd19d3ba82e7673.jpg"},{"id":63371596,"identity":"5c967c72-1656-4d57-81c2-fe29695c8923","added_by":"auto","created_at":"2024-08-27 11:56:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147750,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the number of antenatal care (ANC) visits\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4730450/v1/c149763706f9eeecc85e0025.jpg"},{"id":63370187,"identity":"8765492e-a3fa-4c0b-a451-9947e9def80f","added_by":"auto","created_at":"2024-08-27 11:48:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211168,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Antenatal care visits on different ages with urban-rural disparity\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4730450/v1/2c332cbd69c5de335862160a.jpg"},{"id":63370185,"identity":"d5299ff8-7145-40c5-8ba8-295497c621ea","added_by":"auto","created_at":"2024-08-27 11:48:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159847,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection using the Boruta Algorithm\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4730450/v1/4034067cddbd03fb143c0ea0.jpg"},{"id":83067828,"identity":"b37c59c9-f72e-4370-9d4a-42bad4062b79","added_by":"auto","created_at":"2025-05-19 16:06:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2017438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4730450/v1/d5d357da-4839-4621-af97-1b1807236097.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Sociodemographic and Socioeconomic Determinants of Antenatal Care Utilization in Bangladesh: Insights from the 2017-18 BDHS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaintaining the quality of antenatal care (ANC) is critical to elevating the support provided to women throughout their pregnancy, thereby safeguarding the well-being of both the mother and the baby.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] High-quality ANC impacts pregnancy from both clinical and psychological perspectives, as it helps women prepare for childbirth and the responsibilities of motherhood[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Annually, an estimated 300,000 women succumb to complications stemming from pregnancy or childbirth worldwide, which is around 800 each day on average.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Annually, the South Asian region alone is responsible for over one-third of all maternal and child fatalities worldwide[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The most recent Sustainable Development Goals prioritize declining of global maternal mortality rates to 70 deaths per 100,000 live births and the neonatal mortality rate to 12 deaths per 1000 live births by the year 2030[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. High-quality ANC protects against unexpected pregnancy outcomes. Although 62% of pregnant women globally participated in a minimum of four World Health Organization (WHO)-recommended antenatal care (ANC) activities in 2017, the comparable proportion for Bangladesh stood at only 47%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWidespread access to high-quality prenatal care (ANC) can play a significant role in helping to achieve national and international goals for mother and child health, such as lowering the rate of maternal and newborn mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The Focused Antenatal Care (FANC) Model that was suggested by the World Health Organization (WHO) in the past, it is advised that when a pregnant woman is under typical conditions, she should have at least four ANC visits [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The 2016 WHO ANC Model incorporates eight ANC interactions to help accomplish the Sustainable Development Goals (SDGs) to reduce mother and child mortality[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, Bangladesh still promotes four ANC visits according to the older WHO guidelines[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInequities in using maternal health services are prevalent among developing countries[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Maternal deaths due to inadequate healthcare utilisation are more common among less-educated, poor, and rural populations[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There is a stark disparity in South Asia, with a 33 percentage-point difference between urban and rural areas in the coverage of births attended by skilled health personnel[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The development agenda now prioritizes addressing these enduring disparities, highlighting the necessity of fair access to basic services for all demographic groups. Equitable access is becoming a more important factor in assessing the success of health systems, which is in line with gradually reaching universal health coverage (UHC) with an emphasis on equity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Bangladesh Demographic and Health Survey (BDHS) reveals increased ANC visits from any healthcare facility. ANC coverage rose by 31% from 2004 to 2017 [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Bangladesh continues to be one of the top ten nations in the world for maternal mortality, accounting for approximately 60% of all maternal deaths worldwide, even with this increase[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Many studies have looked into the factors that affect ANC visits and postpartum care in Bangladesh[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The majority of them emphasized the importance of women's education and their wealth status in receiving high-quality prenatal care, as well as the differences in prenatal care by area[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Several studies have revealed that decision-making authority over one's own health care and media access has a significant influence as well[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Exposure to the media has also been found to have an impact on receiving prenatal treatment[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Bangladeshi government is dedicated to accomplishing the Sustainable Development Goals (SDG) as it has presented two Voluntary National Review (VNR) so far[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Over recent decades, socioeconomic disparities and their ramifications on population health, particularly maternal and child health, have emerged as pivotal subjects of concern[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To provide policymakers with assistance in reducing the occurrence of maternal and infant mortality, it is of the utmost importance to identify the socioeconomic and socio-demographic factors that have an impact on maternal health.\u003c/p\u003e \u003cp\u003eWith the assistance of a count regression model, the general goal of our study is to ascertain the degree to which socioeconomic and demographic factors impact the total number of prenatal care visits. To the best of our knowledge, count regression models such as Poisson Regression (PR) and Negative Binomial Regression (NBR) were not utilized in the process of modeling the number of prenatal visits using data from the BDHS (2017-18). So, this investigation will thereby increase the range of evidence in this field.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThe data for this study was extracted from the Bangladesh Demographic and Health Survey, specifically the latest available dataset (BDHS 2017\u0026ndash;2018). The entire population of Bangladesh living in non-institutional housing was covered by the nationally representative Bangladesh Demographic and Health Survey (BDHS) for the 2017-18 fiscal year. The 2011 Bangladesh Bureau of Statistics (BBS) Population and Housing Census provided the sample frame for the study. An enumeration area (EA) is defined as a group of about 120 houses that make up the principal sampling unit (PSU). Bangladesh is divided into eight administrative divisions, which allow the country as a whole to be separated into urban and rural areas[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. .\u003c/p\u003e \u003cp\u003eA two-stage stratified sampling technique was used by the BDHS. According to ICF specifications and BBS implementation, 675 EAs (250 urban and 425 rural) were chosen in the first stage based on a probability proportionate to their size. In order to provide a sample frame for the second step, a thorough household listing was subsequently carried out within these EAs. In the second phase, a total of 20,250 households were carefully chosen in order to produce accurate estimates of population and health for the entire nation, as well as for each division and urban and rural areas separately. Approximately 20,127 married women between the ages of 15 and 49 were interviewed for the survey[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThree clusters were disqualified because of flooding, even though 672 clusters had a successful implementation. There were two rural clusters in Rajshahi and Rangpur and one urban cluster in Dhaka. In the end, the poll covered 20,160 households. Sampling weights were used in the analysis to make sure the data appropriately reflects the national and divisional levels of government. The integrity of the survey results is preserved by these weights, which are intended to reduce any notable variations in survey indicators. For our analysis, 4920 observations were retained after removing the ineligible ones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eThe study's outcome variable was the number of antenatal care visits, representing how frequently expectant mothers received expert medical attention during their pregnancy. This variable provides insights into the quality and accessibility of prenatal care across the population, making it a critical indicator of a mother's health and access to healthcare services[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Researchers can find trends and differences in healthcare access between various demographic groups by examining the number of visits, which can assist in guiding public health initiatives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExplanatory Variables\u003c/h2\u003e \u003cp\u003eThe explanatory variables in the study were categorized into socio-demographic, socio-economic, and contextual factors to provide a structured analysis of the determinants of antenatal care visits. Socio-demographic variables included the respondent\u0026rsquo;s age group (\u0026le;\u0026thinsp;20, 21\u0026ndash;30, \u0026ge;\u0026thinsp;31), place of residence (urban, rural), number of household members (\u0026le;\u0026thinsp;5, 6\u0026ndash;10, \u0026gt;\u0026thinsp;10), birth order (1, 2\u0026ndash;3, 4+), age at first birth (\u0026lt;\u0026thinsp;18, 18\u0026ndash;25, \u0026gt;\u0026thinsp;25), birth in the last three years (1, \u0026gt;\u0026thinsp;1), sex of the household head (male, female), and religion (Islam, Hinduism, Christianity, Buddhism). Socio-economic variables encompassed the highest level of education (no education, primary, secondary, higher), wealth index (poor, middle, rich), partner\u0026rsquo;s education level (no education, primary, secondary, higher), husband\u0026rsquo;s occupation (unemployed, agricultural or household work, service and sales, others), employment status (yes, no), and health insurance (yes, no). Contextual factors included the division (Dhaka, Chittagong, Khulna, Rajshahi, Mymensingh, Rangpur, Sylhet, Barisal), whether the pregnancy was wanted at that time (yes, no), cesarean section at previous birth (yes, no), and coverage of media (yes, no). A strong framework for examining the factors influencing the use of prenatal care in Bangladesh was provided by these variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFirst, the distribution of the number of antenatal care visits was analyzed by a bar plot. A bivariate analysis was performed using either the Mann-Whiteney or Kruskall-Wallis test to ascertain the median number of visits for each category of covariates. The Kruskal-Wallis and Mann-Whitney tests are non-parametric methods for evaluating median differences between groups, with the former suitable for more than two groups and the latter for two groups[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Boruta Algorithm, a machine learning feature selection method, was applied to choose covariates for modeling the data. This technique utilizes a random forest approach to determine feature importance by comparing actual features' accuracy loss to randomly shuffled shadow features, thus identifying critical attributes amidst random variations.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter that, using the Akaike Information Criterion (AIC) and log-likelihood, we assessed three regression models such as Poisson regression (PR), Negative binomial regression (NBR), and Multiple linear regression (MLR). The Akaike information criterion is a mathematical framework used for model selection and parsimony assessment in model construction.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Since the negative binomial regression model had a lower AIC value, we finally used it for parameter estimation and interpretation. A flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was used to illustrate the entire study. Stata 17 and R 4.3.1 were the statistical tools used during the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Analysis","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the distribution of antenatal care visits, revealing that over 50% of women do not receive the WHO-recommended minimum of at least four. This discrepancy is a significant concern that warrants attention. The distribution is positively skewed here.\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\u003e\u003cb\u003ePercentage distribution of the respondents and median number of antenatal visits by subgroups\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,204 (24.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,883 (58.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e833 (16.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDivision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarisal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e524 (10.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChittagong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e814 (16.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e728 (14.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhulna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e510 (10.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMymensingh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e594 (12.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRajshahi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519 (10.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRangpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e550 (11.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSylhet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e681 (13.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,692 (34.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,228 (65.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304 (6.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,364 (27.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,358 (47.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e894 (18.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIslam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,503 (91.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.0587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHinduism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e392 (7.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuddhism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (0.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristianity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (0.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of household members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,482 (50.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,079 (42.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e359 (7.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHousehold head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,337 (88.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.2355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e583 (11.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,058 (41.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e882 (17.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,980(40.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBirth Order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,864 (37.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,458 (49.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e598 (12.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(continued)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge at first birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,990 (40.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,730 (55.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200 (4.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBirth in three years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,635 (94.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285 (5.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePregnancy wanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,876 (78.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e642 (13.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e402 (8.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCesarean section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,275 (66.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,645 (33.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,911(99.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (0.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHusband\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e678 (13.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,654 (33.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,633 (33.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e955 (19.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,081 (62.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,839 (37.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedia Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,761 (35.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,159 (64.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHusband\u0026rsquo;s Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural or Household work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e924 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService and Sales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,032 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,926 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the percentage distribution of respondents and the median number of antenatal visits across various subgroups based on different factors. About 24.47% of respondents are aged 20 or younger, 58.60% are between 20 and 30, and 16.93% are older than 30. The median number of antenatal visits is the same (3 visits) across all age groups. The p-value (0.0019) indicates a significant difference in the median antenatal visits across different age groups. The respondents are distributed across various divisions. Dhaka, Khulna, Rajshahi, and Rangpur divisions have the highest median number of antenatal visits (4 visits), while the divisions of Barisal and Sylhet have the lowest median (3 visits). The p-value (0.0001) suggests a significant difference in the median number of antenatal visits across divisions. More respondents reside in rural areas (65.61%) compared to urban areas (34.39%). Urban residents have a higher median number of antenatal visits (4) than rural residents (3). Most respondents have secondary education (47.93%), followed by primary (27.72%) and higher education (18.17%). Respondents with higher education have a higher median number of antenatal visits (5 visits), followed by secondary (4 visits) and primary education (2 visits). Although insignificant (p\u0026thinsp;=\u0026thinsp;0.0587), Muslims constitute the majority (91.52%) with a median of 3 visits, while other religions have lower representation and slightly higher median visits. There's no significant difference in visit frequency by gender of household head (p\u0026thinsp;=\u0026thinsp;0.2355). This suggests that the gender of the household head may not significantly influence antenatal care utilization. Wealthier women had more visits (median\u0026thinsp;=\u0026thinsp;4) compared to poorer ones (median\u0026thinsp;=\u0026thinsp;2), and this difference is significant (p\u0026thinsp;=\u0026thinsp;0.0001). For the Firstborns, women had the highest median visits (4), followed by 2\u0026ndash;3 (3) and 4+ (2). Those aged 18\u0026ndash;25 have the most visits (median\u0026thinsp;=\u0026thinsp;4) compared to others. The vast majority of respondents (94.21%) reported giving birth once within three years. There's a significant difference in visit frequency based on pregnancy intention (p\u0026thinsp;=\u0026thinsp;0.0001), with those intending to have a child later or no more children having fewer visits than those who wanted the pregnancy then. A significant proportion of respondents who underwent a cesarean section (33.43%) had more visits. Most husbands had primary or secondary education, and there's a considerable difference in visit frequency based on the husband's education level (p\u0026thinsp;=\u0026thinsp;0.0001), with higher-educated husbands being associated with more antenatal care visits. Most respondents had media coverage (64.21%) with more visits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eVariable Selection:\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe output of the Boruta Algorithm in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e suggests that the number of births in the last three years, religion, sex of household head, and health insurance are deemed unimportant in predicting the number of antenatal care visits. However, variables like the husband\u0026rsquo;s occupation and respondent's employment status show some indication of importance but are not confirmed. The remaining variables are considered important predictors of the number of antenatal visits. These findings led us to build a model by focusing on the confirmed important variables while considering the tentative ones cautiously.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eModel Selection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of models based on Akaike\u0026rsquo;s information criteria (AIC) and log Likelihood\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog Likelihood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoodness of fit\u003c/p\u003e \u003cp\u003etest (P value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22708.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11322.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22047.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10990.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23221.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-11565.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the results of model selection criteria for the three initially fitted models (PR, NBR, MLR). Among these models, it is observed that the Negative Binomial Regression (NBR) has the lowest AIC value. Therefore, the Negative Binomial Regression model was selected to examine the variables influencing changes in the frequency of prenatal care visits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFitted Negative Binomial Regression Model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncident Rate Ratios (IRR) of different subgroups based on Negative Binomial Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI for IRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP- value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=20 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDivision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarisal(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChittagong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhulna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMymensingh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRajshahi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRangpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSylhet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of household members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=5(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBirth Order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge at first birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePregnancy wanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethen(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCesarean section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHusband\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedia Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eref: Reference category\u003c/h2\u003e \u003cp\u003eThe significant demographic, socioeconomic, and pregnancy-related components of fitted negative binomial regression are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, along with the incidence rate ratios (IRR) for each. Age group, division, place of residence, education level, number of household members, wealth index, birth order, age at first birth, intention to conceive, cesarean section, partner\u0026rsquo;s education, and exposure to media were independently associated with the number of antenatal visits. Women between 20 and 30 had 8.5% higher antenatal visits than those under 20 (IRR 1.085, 95% CI 1.025\u0026ndash;1.149). Compared to the Barisal division, women from Khulna, Mymensingh, and Rangpur received 14.4%, 11.9%, and 29% more visits, respectively. (IRR 1.14, 95% CI 1.055\u0026ndash;1.239; IRR 1.119, 95% CI 1.034\u0026ndash;1.211 and 1.29, 95% CI 1.192\u0026ndash;1.395). Comparing women in rural and urban areas, the former had 12% fewer visits (IRR 0.88, 95% CI 0.84\u0026ndash;0.92). Women with secondary and higher education had more than 40% antenatal visits compared to those without education (IRR 1.42,95% CI 1.28\u0026ndash;1.56 and 1.46, CI 1.31\u0026ndash;1.64). The number of household members does not significantly affect the incidence rate. Middle-class and wealthy women had 8% and 13% more visits than the poor. The incidence rate is 0.943 times lower for birth order of 2\u0026ndash;3 and 0.830 times lower for birth order of 4\u0026thinsp;+\u0026thinsp;than for birth order of 1 (IRR 0.94, 95% CI 0.89-.99 and .82, 95% CI 0.75\u0026ndash;0.91). The age at first birth does not significantly affect the incidence rate. Women who wanted to wait to get conceived and didn't want to have more children had 15% and 8% fewer visits, respectively, than women who wanted to get conceived right away (0.85, 95% CI 0.79\u0026ndash;0.92 and 0.92, 95% CI 0.87\u0026ndash;0.97). Individuals with a cesarean section had 28% more visits than those without (IRR 1.28, 95% CI 1.23\u0026ndash;1.34). Compared to those who are illiterate, those whose husbands have completed secondary or higher education had 13% and 20% more visits, respectively (IRR 1.13, 95% CI 1.05\u0026ndash;1.21 and 1.20, 95% CI 1.11\u0026ndash;1.31). Those in media coverage had 20% more visits than those without (1.20, 95% CI 1.14\u0026ndash;1.25).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study has identified several sociodemographic and socioeconomic variables that are significantly associated with the frequency of antenatal care (ANC) visits in Bangladesh. The findings reveal that less than 50% of women meet the World Health Organization (WHO) recommended minimum of four ANC visits, which aligns with the results of Akter et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This percentage is notably lower than in neighbouring countries, such as India (59.25%) and Nepal (69%), but similar to Pakistan[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key finding of this study is the pronounced urban-rural disparity in ANC visits, which is clearly illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Urban women were found to have 12% more visits than their rural counterparts. This disparity can be attributed to various factors, including better availability and accessibility of medical facilities, higher socioeconomic status, and greater educational attainment in urban areas[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEducation, particularly for women, emerged as a critical determinant of ANC quality. Women with secondary or higher education levels were 42% and 46% more likely to attend ANC visits than those without or only primary education. Another study by Haque et al. also found that the frequency of ANC visits was 10.6% lower for mothers who did not continue their education after marriage[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Educated women are generally more informed about health issues and the benefits of medical care over traditional treatments[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe wealth status of women is found to be another significant determinant since middle-class and rich women had 8% and 13% more visits compared to poor, which is also found in several studies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Poor women often cannot afford the costs associated with high-quality ANC, including consultation fees, diagnostic tests, medications, and transportation to healthcare facilities[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Birth order was also significantly related to the frequency of ANC visits. Mothers who already have children were less likely to have frequent ANC visits, which was also found in other studies[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] A study in Ethiopia revealed that higher birth order was inversely related to the timing of the first ANC visit, which led to fewer ANC visits[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study discovered that a woman\u0026rsquo;s age affects how frequently she visits an ANC. Compared to younger mothers, older mothers were more likely to have frequent ANC visits. However, the age at first birth showed a different scenario. Women who gave first birth before 18 were more likely to have quality care than who gave later, though it was not highly significant.\u003c/p\u003e \u003cp\u003eIn addition, our study showed that women who desired a pregnancy later or who did not want more were less likely to visit frequently than those who did. This is also found in two studies by Biswas et al. and Islam et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Unintended pregnancies are often linked to delayed initiation and insufficient use of antenatal care services[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Women with unintended pregnancies may be less prepared or less motivated to seek timely and regular prenatal care, leading to poorer maternal and child health outcomes\u003c/p\u003e \u003cp\u003eHaving a Caesarean section is another significant element in having more ANC visits. Women who had a Caesarean section had 28% more ANC visits than who did not. Healthcare providers typically recommend more frequent ANC visits for women with a history of Caesarean section to ensure any potential complications are detected and managed early[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe degree of education of a partner is equally important to ANC as the education of women. Women with highly educated partners tend to visit the ANC more frequently than those with less educated partners. Similar to a number of other studies, ours discovered media coverage to be a key influence[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Exposure to media increases awareness and knowledge about the importance of regular ANC visits, thereby encouraging more women to seek timely and comprehensive prenatal care[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Parallel to a study by Ali et al., we did not discover any significant association between the number of household members and the quality of ANC[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrength and limitations\u003c/h2\u003e \u003cp\u003eThe main advantage of this study is that it used data that is representative of the entire country. In addition, we have evaluated three distinct statistical models and determined which one is the most effective in terms of estimating parameters and, consequently, interpreting the data. While the Negative Binomial Regression Model was utilized for the final parameter estimation, we failed to consider the inflation of zeros in the response variable, which would have been more appropriate if a zero-inflated model had been employed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the significant impact of various sociodemographic and socioeconomic factors on antenatal care (ANC) visits in Bangladesh. Despite progress, the proportion of women meeting the WHO-recommended minimum ANC visits remains below 50%. Our findings indicate a notable urban-rural disparity, with rural women attending fewer ANC visits than their urban counterparts. Factors such as education level, wealth status, birth order, age, pregnancy intentions, and cesarean section history significantly influence ANC visit frequency. Women with higher education, better wealth status, and a history of cesarean sections are more likely to attend ANC visits. Conversely, higher birth order and unintended pregnancies reduce the likelihood of frequent ANC visits. These insights underscore the need for focused initiatives to address disparities and enhance ANC coverage, particularly among rural, less educated, and poorer women. By addressing these factors, policymakers can improve maternal health outcomes and work towards achieving national and international health goals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntenatal care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBangladesh Demographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePoisson Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNBR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative Binomial Regression Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple Linear Regression Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncidence Rate Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSustainable Development Goals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available data from the Bangladesh Demographic and Health Survey (BDHS) 2017. The survey was ethically approved by the Institutional Review Board of ICF International and the Ethics Review Committee of NIPORT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this article is sourced from the DHS Program database, which is accessible at https://dhsprogram.com/Data/ . The data sets utilized in the current analysis are available from the corresponding author upon reasonable request. For additional information, please contact the author in question.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting \u0026nbsp; interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that no known competing interests or personal relationships could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Md. Mojammel Haque Sakib: Conceptualization, Methodology, Data analysis, Writing - original draft\u003c/p\u003e\n\u003cp\u003eMuhammad Khairul Alam: Conceptualization, Methodology, Data analysis, Writing - original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMst. Nilufar Yasmin: Conceptualization, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eRumana Rois: Conceptualization, Methodology, Supervision, Writing - original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Bangladesh Demographic and Health Survey (BDHS) team for providing the data that was essential for this research. Their comprehensive data collection and dissemination efforts have been invaluable to our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkter E, Hossain AT, Rahman AE, Ahmed A, Tahsina T, Tanwi TS et al. Levels and determinants of quality antenatal care in Bangladesh: Evidence from the Bangladesh Demographic and Health Survey. PLoS One. 2023;18 5 MAY.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeeckman K, Louckx F, Putman K. 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PLoS ONE. 2022;17:e0262323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryce E, Katz J, Pema Lama T, Khatry SK, LeClerq SC, Munos M. Antenatal care processes in rural Southern Nepal: gaps in and quality of service provision\u0026mdash;a cohort study. BMJ Open. 2021;11:e056392.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirotra S, Malik M, Roy S, Basu S. Utilization and determinants of adequate quality antenatal care services in India: evidence from the National Family Health Survey (NFHS-5) (2019-21). BMC Pregnancy Childbirth. 2023;23:800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKibria GM, Al, Nayeem J. Association of rural-urban place of residence with adequate antenatal care visit in Bangladesh. PLOS Global Public Health. 2023;3:e0002528.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaque ME, Mallick TS, Bari W. Does dropout from school matter in taking antenatal care visits among women in Bangladesh? An application of marginalized poisson-poisson mixture model. BMC Pregnancy Childbirth. 2022;22:476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhowmik J, Biswas RK, Woldegiorgis M. Antenatal care and skilled birth attendance in Bangladesh are influenced by female education and family affordability: BDHS 2014. Public Health. 2019;170:113\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MA, Kabir MR, Talukder A. Triggering factors associated with the utilization of antenatal care visits in Bangladesh: An application of negative binomial regression model. Clin Epidemiol Glob Health. 2020;8:1297\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo Y, Alland K, Ali H, Mehra S, Lefevre A, Pak S et al. Antenatal care in rural Bangladesh: current state of costs, content and recommendations for effective service delivery. BMC Health Serv Res. 2019;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldeamanuel BT, Belachew TA. Timing of first antenatal care visits and number of items of antenatal care contents received and associated factors in Ethiopia: multilevel mixed effects analysis. Reprod Health. 2021;18:233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDibaba Y, Fantahun M, Hindin MJ. The effects of pregnancy intention on the use of antenatal care services: systematic review and meta-analysis. Reprod Health. 2013;10:50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabbro MRC, Wernet M, Baraldi NG, de Castro Bussadori JC, Salim NR, Souto BGA, et al. Antenatal care as a risk factor for caesarean section: a case study in Brazil. BMC Pregnancy Childbirth. 2022;22:731.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFatema K, Lariscy JT. Mass media exposure and maternal healthcare utilization in South Asia. SSM Popul Health. 2020;11:100614.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antenatal Care Visit, Maternal Health, Bangladesh Demographic and Health Survey, Negative Binomial Regression","lastPublishedDoi":"10.21203/rs.3.rs-4730450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4730450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAntenatal care (ANC) is indispensable for supervising and enhancing the health of both the mother and the baby during pregnancy. It helps to reduce the risks of complications and ensures better pregnancy outcomes. This study investigates the aspects that influence antenatal care (ANC) visits in Bangladesh, focusing on sociodemographic and socioeconomic factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study used the most current, nationally representative data from the 2017\u0026ndash;18 Bangladesh Demographic and Health Survey (BDHS). Mann-Whitney and Kruskal-Wallis tests were conducted for bivariate analysis. The Boruta algorithm was utilized for variable selection. After employing various regression models, including Poisson Regression (PR), Negative Binomial Regression (NBR), and Multiple Linear Regression (MLR), we evaluated their performance and selected Negative Binomial Regression for parameter estimation and interpretation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results reveal that less than 50% of women meet the WHO-recommended minimum number of ANC visits. Women with secondary and higher education (IRR 1.42 \u0026amp; 1.46, 95% CI 1.28\u0026ndash;1.56 \u0026amp; 1.31\u0026ndash;1.64), Rich wealth status (IRR 1.13, 95% CI 1.07\u0026ndash;1.19), Cesarian section (IRR 1.28, 95% CI 1.23\u0026ndash;1.34), media coverage (IRR 1.20, 95% CI 1.14\u0026ndash;1.25) were more likely to have frequent ANC visits. Conversely, women with higher birth order (IRR 0.94 \u0026amp; 0.82, 95% CI 0.89\u0026ndash;0.99 \u0026amp; 0.75\u0026ndash;0.91), unintentional pregnancy (IRR 0.92 \u0026amp; 0.85, 95% CI 0.87\u0026ndash;0.97 \u0026amp; 0.79\u0026ndash;0.92) were less likely to have ANC vists.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGiven that the majority of women in Bangladesh do not receive adequate antenatal care, achieving national and international maternal and child health goals will be challenging. This study identified factors hindering access to high-quality prenatal care, which the Bangladeshi administration should address through focused actions.\u003c/p\u003e","manuscriptTitle":"Unveiling the Sociodemographic and Socioeconomic Determinants of Antenatal Care Utilization in Bangladesh: Insights from the 2017-18 BDHS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:48:54","doi":"10.21203/rs.3.rs-4730450/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-25T18:33:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-23T05:25:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-19T18:34:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52146589992997872061601323796257634702","date":"2024-12-10T04:42:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34121383074193712577458347417694704086","date":"2024-12-01T04:13:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36166851384919593636798853807603556574","date":"2024-11-30T14:41:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-30T14:28:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-25T13:58:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-25T13:57:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2024-07-12T12:56:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9743eb4f-6c1f-415b-84fb-5c2dbef3aa18","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:01:46+00:00","versionOfRecord":{"articleIdentity":"rs-4730450","link":"https://doi.org/10.1186/s41043-025-00839-w","journal":{"identity":"journal-of-health-population-and-nutrition","isVorOnly":false,"title":"Journal of Health, Population and Nutrition"},"publishedOn":"2025-05-16 15:57:50","publishedOnDateReadable":"May 16th, 2025"},"versionCreatedAt":"2024-08-27 11:48:54","video":"","vorDoi":"10.1186/s41043-025-00839-w","vorDoiUrl":"https://doi.org/10.1186/s41043-025-00839-w","workflowStages":[]},"version":"v1","identity":"rs-4730450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4730450","identity":"rs-4730450","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}