Birth-order based determinants of caesarean sections in Bangladesh: A multilevel mixed effect regression approach

Birth-order based determinants of caesarean sections in Bangladesh: A multilevel mixed effect regression approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Birth-order based determinants of caesarean sections in Bangladesh: A multilevel mixed effect regression approach Shanjida Chowdhury, Md. Asrafur Rahman Ashiq, Md. Aminul Haque This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8350383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The use of Caesarean section (CS) in Bangladesh has increased significantly above the medically justified levels and it varies by socioeconomic background. However, there is little research on the determinants of CS that differ by parity, although there are solid clinical and social arguments that parity-specific effects are likely. This paper used a multilevel-based investigation to explore birth-order-specific determinants of CS in Bangladesh. Methods This study used data from Bangladesh Demographic and Health Survey 2022 to explore the determinants of caesarean section in Bangladesh. The binary outcome variable of the study was whether the respondent had CS in last birth or not. To detect heterogeneity by birth order, the dataset was stratified into two groups; first births and second or higher births. Separate multilevel mixed effect models were then fitted for each group to identify how CS varied by parity. Results The results showed that 45.5% of births were delivered by CS. This rate was higher for first births (52.4%) than for second or higher-order births (41.4%). The community level model performed better for both the stratified models. For first births, age of mothers at birth, higher educational attainment, size of the baby at birth, and antenatal care visits were significant predictors of CS, while rural–urban disparities were reduced after adjustment. For second or higher-order births, higher maternal education, ANC (≥ 4 visits), exposure to the mass media, richer wealth status, and urban residence were important predictors of CS, with notable regional variations (lower odds in Chattogram and Sylhet compared with Dhaka). Conclusion These findings highlight the need for parity-specific, context-specific strategies to reduce unnecessary CS while ensuring access for women in genuine need, particularly by addressing socioeconomic and regional inequities in maternal healthcare. cesarean section parity specific multilevel mixed effect logistic regression BDHS Bangladesh Figures Figure 1 Contribution to the literature Although previous literatures in Bangladesh explored the socio-demographic determinants of CS, the role of parity on CS has not been analyzed separately. This study addresses the gap and aims to contribute to the literature by discovering the factors associated with CS and assessing how these determinants vary by the birth order of the child. Introduction The caesarean section (CS) is an operation in cases where medical conditions like obstruction of labor or fetal distress occur. In recent decades, the number of CS has been growing rapidly in most countries, often without medical justification. This trend has the potential to heighten the risk of maternal and neonatal health, overstretch health resources, and place significant financial pressure on families and health systems [ 1 ]. Data analysis of 169 countries suggests that approximately 21 per cent of all births worldwide are currently C-section-delivered, amounting to almost 30 million caesarean births each year, and the percentage of this group may rise to nearly 28–29 per cent by 2030, assuming current trends continue [ 2 ]. These levels substantially exceed the 10–15% range that the World Health Organization (WHO) has historically regarded as the optimum population-level rate compatible with improved maternal and perinatal outcomes [ 3 ]. In low- and middle-income countries (LMICs), including those in South Asia, health systems now face a dual challenge, with some women who genuinely need CS still not getting it. In contrast, others undergo CS primarily for non-clinical reasons [ 1 ]. Bangladesh reflects this mixed picture. National surveys and hospital-based studies show that CS use has risen sharply in recent years, with exceptionally high rates in private facilities and urban settings. Caesarean section (CS) rates in Bangladesh have increased dramatically, from about 3% in 2000 to over 33% by 2017/18, with recent estimates in some urban and private facilities exceeding 50–80% [ 4 – 7 ]. Numerous studies in the Bangladesh and similar LMICs have investigated individual, as well as community-based determinants of C-section application, such as maternal age, education, household wealth, parity, use of antenatal care, and place or sector of delivery. Multilevel and other modelling techniques have reported strong links of C-section to maternal age, greater socioeconomic status, increased frequency of antenatal visits, and birth in private institutions [ 8 ]. In a study conducted in a hospital in the Jashore District, a significant proportion of births was delivered by CS and revealed the maternal age, education, use of the antenatal care (ANC), and delivery in a privately owned facility as significant predictors [ 9 ]. The findings can be added to the broader evidence that in Bangladesh, women in richer families have a higher likelihood of having CS, with higher education levels and more frequent visits to ANC indicating that social and health-system factors have a significant impact on mode of delivery [ 9 – 11 ]. In 2020, per day about 800 and annually 287000 women lost their live during and following pregnancy and childbirth [ 12 , 13 ]. The lifetime risk of dying from pregnancy and childbirth related cause is estimated to be greater in developing countries compared to developed countries as developing countries account for 94–99% of all maternal deaths globally [ 14 , 15 ]. Although there is already significant research on the determinants of CS, a severe gap remains: the impact of birth order has been explored only as a simple covariate. In the majority of investigations, all births are treated as a homogeneous group, and a single common model is estimated; this method assumes that the determinants of CS are the same for first and subsequent births [ 16 , 17 ]. However, there is some evidence that the mode-of-delivery depends on the decision-making process[ 18 ], clinical indications [ 16 ], and social expectations [ 18 , 19 ] are very different. Primiparous (first-time mothers) women can be more vulnerable to defensive medical practice [ 19 , 20 ], fear of labor problems [ 19 ], or provider advice [ 19 , 20 ]. On the contrary, higher-order births among women introduce previous birth experiences [ 21 ], past CS history [ 22 , 23 ], cumulative maternal morbidity [ 23 ], and changing fertility intentions [ 23 ], which may shape their preferences as well as clinical manifestation. Such parity-specific processes are especially relevant in Bangladesh, where fertility has fallen, childbearing is early and good social value is placed on successful first births. Determining the variation in CS use by birth order and the interactions of this variation with other socioeconomic, demographic, and health-system factors is essential for identifying medically necessary procedures and those that may be avoided. Nevertheless, the available literature in Bangladesh sheds little light on these processes, leaving the drivers of increasing parity-specific CS rates unexplored. Analytically, data on reproduction and healthcare in Bangladesh are stratified; births are contained within mothers, mothers within households, and households within communities. A multilevel regression model thus provides a good fit for identifying individual and contextual determinants of CS [ 24 – 26 ]. This will enable the researcher not only to evaluate the maternal attributes, including age, education, wealth, ANC use, and obstetric history, but also the more advanced factors, such as region, community socioeconomic status, and the influence of the local environment of the public-private facility [ 8 ]. With the introduction of random effects, multilevel models can measure the degree to which the context of communities or facilities, not observable at the individual level, contributes to CS use, not just individuals. To fill the above-identified gaps, the present study will use a birth-order-stratified multilevel modelling approach to test the determinants of CS in Bangladesh. We do not use a single pooled model; instead, we estimate individual first-, second-, and higher-order births. This design enables key predictors to vary by parity, providing more insight into how socioeconomic and health-system factors act differently across birth orders. The joint use of birth-order-specific analysis and multilevel modelling has not been studied before in Bangladesh to determine the CS determinants. This new paradigm offers a more subtle perspective on the interaction among individual factors, situational forces, and parity-specific forces that shape CS use. These insights will be essential for developing specific interventions to enhance medically appropriate, equitable, and cost-effective delivery of care in Bangladesh. Methods of the study 2.1 Data source and sample This study used nationally representative Bangladesh Demographic and Health Survey (BDHS) 2022 to identify the determinants of CS in Bangladesh by birth order. The BDHS employed a two-stage stratified sampling technique to ensure representativeness at the national and divisional levels [ 27 ]. The IR dataset of the BDHS 2022 was utilized to collect the variables of the study. Initially, the dataset had 30,078 women respondents. After deleting the missing values on important variables, 4915 weighted observations remained for the analysis. Out of which, 1824 observations were used to find out the determinants of CS for first birth order, and 3091 observations were used to extract the determinants of CS for second or higher birth orders. As caesarean section differs significantly between primiparous and multiparous women, this study fitted model separately for respondents of each parity. This stratified approach is consistent with prior evidence that birth order is a major determinant and modifier of obstetric risk [ 28 – 31 ]. 2.2 Variables of the Study The outcome variable of the study was whether the respondent had Caesarean section (CS) in her last birth. If she had CS in her last birth, she was coded as 1 and if not, she was coded as 0. The main group variable of the study was the birth order of the last birth of the respondent. The original data was separated into two datasets on the basis of first birth order and second or higher birth orders. An extensive literature review was conducted to identify the relevant explanatory variables for the study. The predictor variables included in the study were, age of mother at birth, educational attainment of mother, size of the baby at birth, antenatal care visit, mass media exposure, wealth index of the household, place of residence, and administrative division of residence [ 10 , 29 , 32 ]. 2.3 Statistical analysis Descriptive statistics were conducted to summarize the socio-demographic characteristics of the respondents. Multilevel mixed effect logistic regression was fitted later on the basis of birth order to identify the determinants of CS in Bangladesh [ 8 , 33 ]. Multilevel analysis is an appropriate statistical method for research designs in which participant data is structured across multiple levels. Multilevel logistic regression was applied to adequately handle the hierarchical structure of BDHS data, where the observations were nested within households, which were nested within regions [ 34 ]. The problem of dependencies between individual observations often comes from several levels of hierarchy. The dependence also occurs in survey research like BDHS, where the sample was not taken randomly, but cluster sampling from geographical areas was used instead. Traditional regression models assume independence of observations, which would be appropriate for survey research. Hence, this study utilized multilevel regression model to identify the determinants of CS in Bangladesh, which allowed us to control for the potential correlation of individuals within higher level units. In this study, explanatory variables were summarized in three levels: individual level, household level, and community level. The levels were comprised of individual level: Age of mother, education of mothers, size of the baby at birth, antenatal care visits, household level: Mass media exposure, wealth index of the household, and community level: Place of residence, division. In this study, multilevel mixed effect logistic regression was utilized to determine the factors responsible for CS in Bangladesh, as the dataset was multistage cluster survey. Let, \(\:{Y}_{ij}\:\) be the outcome variable measured on the ith subject within jth cluster ( \(\:{Y}_{ij}\) =1, if the respondent had CS in her last birth, \(\:{Y}_{ij}\) =0, if she did not). Furthermore, \(\:{X}_{1ij},\:\dots\:,\:{X}_{kij}\:\) are k independent variables measured on different levels. Then the mixed effect model is, $$\:\text{log}\left(\frac{{\pi\:}_{ij}\:}{1-{\pi\:}_{ij}}\right)=\:{\beta\:}_{0}+{\beta\:}_{1}{X}_{1ij}+\:\dots\:+\:{\beta\:}_{k}{X}_{kij}+{u}_{oj}+{e}_{ij}$$ 1 Here, \(\:{\pi\:}_{ij}\:\) is the likelihood of occurring CS in the last birth, \(\:{\beta\:}_{1,\:}\dots\:,\:{\beta\:}_{k}\) are the effect sizes of individual and community levels, \(\:{u}_{oj}\) are the random errors at cluster levels, and \(\:{e}_{ij}\) is the random errors at the individual levels [ 35 ]. All statistical analyses were conducted at R 4.5.2. The ‘glmmTMB’ package was utilized to fit the mixed effect logistic regression model [ 36 ]. Variance inflation factor (VIF) was checked for multicollinearity, and it was found that all of the explanatory variable had VIF < 2. Result Descriptive Statistics and bivariate association Table 1 shows the descriptive characteristics of the respondents. The prevalence of Caesarean section was found to be 45.5%. According to the Fig. 1 , the prevalence of CS was found higher in first birth order than the second or higher birth orders (52.4% vs 41.4%), which revealed significant difference in CS on the basis of birth order of the children (p-value < 0.001). Moreover, the CS was found to be more frequent among mothers with higher educational attainment (70.2%) than among those with lower educational attainment. A higher prevalence of CS was also observed among mothers with more than 4 ANC visits, mass media exposure, a wealthy household, and urban residence in Dhaka, Khulna, and Rajshahi divisions. Table 1 Descriptive characteristics and caesarean section among mothers CS No Yes Total p-value Birth order of the last birth First order 869 47.6% 955 52.4% 1824 100.0% < 0.001 Second or more 1810 58.6% 1281 41.4% 3091 100.0% Total 2679 54.5% 2236 45.5% 4915 100.0% Age of mother at birth <=19 547 57.2% 410 42.8% 957 100.0% 0.141 20–24 878 53.4% 767 46.6% 1645 100.0% 25–29 673 53.0% 598 47.0% 1271 100.0% 30+ 581 55.8% 461 44.2% 1042 100.0% Education of mothers No education 200 77.5% 58 22.5% 258 100.0% < 0.001 Primary 808 71.7% 319 28.3% 1127 100.0% Secondary 1391 53.7% 1198 46.3% 2589 100.0% Higher 280 29.8% 661 70.2% 941 100.0% Size of child at birth Larger than average 250 52.0% 231 48.0% 481 100.0% 0.232 Average 2055 54.4% 1723 45.6% 3778 100.0% Smaller than average 374 57.0% 282 43.0% 656 100.0% Antenatal Care Visit 0 348 90.2% 38 9.8% 386 100.0% < 0.001 1–3 1541 61.2% 975 38.8% 2516 100.0% 4+ 790 39.2% 1223 60.8% 2013 100.0% Mass Media Exposure No 1413 66.4% 716 33.6% 2129 100.0% < 0.001 Yes 1266 45.4% 1520 54.6% 2786 100.0% Wealth index Poor 1410 70.4% 594 29.6% 2004 100.0% < 0.001 Middle 542 55.2% 440 44.8% 982 100.0% Rich 727 37.7% 1202 62.3% 1929 100.0% Place of residence Rural 1952 59.2% 1346 40.8% 3298 100.0% < 0.001 Urban 727 45.0% 890 55.0% 1617 100.0% Division Dhaka 337 46.4% 389 53.6% 726 100.0% < 0.001 Barishal 298 55.5% 239 44.5% 537 100.0% Chattogram 560 66.0% 289 34.0% 849 100.0% Khulna 187 33.6% 369 66.4% 556 100.0% Mymensingh 355 58.5% 252 41.5% 607 100.0% Rajshahi 219 43.9% 280 56.1% 499 100.0% Rangpur 318 55.9% 251 44.1% 569 100.0% Sylhet 405 70.8% 167 29.2% 572 100.0% Multilevel mixed model for first birth order Three progressively adjusted multilevel mixed effect models were fitted to examine the determinants of CS in mothers of reproductive age in their first birth order (Table 2 ). Age of mothers at birth, higher educational attainment, size of the baby at birth, and antenatal care visits were significant predictors of CS in first birth order from the individual level in the full adjusted model. Age of mother at first birth played a noteworthy role in determining CS, showing increasing trend of odds with the increase of age, with 2.86 times higher CS for 30 + aged mothers than mothers aged 15–19 years. Although higher educational attainment was found to be significantly associated with 2.48 times higher odds of CS than no education at the individual level, it was found to be insignificant after adjusting for household and community level predictors. Average size of the baby at birth revealed a substantially lower probability of CS (AOR: 0.56) in the first birth than in larger than average size of the baby at birth. More than four of the antenatal care (ANC) visits of mothers more than four were also major contributors to CS in first birth, with almost 7 times higher likelihood of CS than mother with no ANC visit. Between the household level predictors media exposure and wealth index, mothers from the rich households had a 56% higher likelihood of CS in first birth than the mothers of poor households. Although mass media exposure was found to be significant with 36% higher odds of CS in first birth order, it became insignificant after adjusting for community-level predictors in the model. However, marked regional disparities in CS were observed for first birth. While mothers from Khulna and Rajshahi divisions had higher odds of CS in the first birth than Dhaka, Chattogram, and Sylhet divisions showed lower odds of CS in the first birth. Nevertheless, once socioeconomic and maternal factors were controlled, the urban-rural difference in CS diminished as place of residence was found to be insignificant in the fully adjusted model. Table 2 Multilevel Mixed model for first birth order caesarean section Individual level Household level Community level Predictors Levels Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p Age of mother at birth (Ref: 15–19 years) 20–24 1.47 1.16–1.86 0.001 1.45 1.15–1.83 0.002 1.62 1.28–2.05 < 0.001 25–29 2.06 1.37–3.08 < 0.001 2.03 1.35–3.04 0.001 2.31 1.54–3.47 < 0.001 30+ 2.82 1.40–5.67 0.004 2.54 1.27–5.10 0.009 2.86 1.43–5.72 0.003 Education of mothers (Ref: No education) Primary 0.99 0.43–2.28 0.983 0.95 0.41–2.16 0.896 0.90 0.39–2.04 0.795 Secondary 1.63 0.73–3.62 0.234 1.43 0.64–3.16 0.382 1.30 0.59–2.88 0.515 Higher 2.48 1.09–5.62 0.030 1.95 0.86–4.42 0.112 1.71 0.75–3.88 0.199 Size of the baby at birth (Ref: Larger than average) Average 0.56 0.39–0.80 0.001 0.55 0.38–0.78 0.001 0.56 0.39–0.80 0.001 Smaller than average 0.47 0.30–0.73 0.001 0.48 0.31–0.75 0.001 0.52 0.33–0.81 0.004 Antenatal care visit (Ref: 0) 1–3 4.32 2.14–8.72 < 0.001 3.88 1.92–7.82 < 0.001 3.92 1.94–7.90 < 0.001 4+ 8.16 4.02–16.58 < 0.001 6.81 3.34–13.86 < 0.001 6.91 3.39–14.07 < 0.001 Mass media exposure (Ref: No) Yes 1.36 1.09–1.69 0.007 1.20 0.96–1.50 0.115 Wealth index (Ref: Poor) Middle 1.23 0.93–1.64 0.152 1.24 0.93–1.66 0.142 Rich 1.49 1.14–1.93 0.003 1.56 1.18–2.06 0.002 Place of residence (Ref: Rural) Urban 0.87 0.68–1.11 0.265 Division (Ref: Dhaka) Barishal 0.84 0.54–1.31 0.442 Chattogram 0.55 0.38–0.81 0.003 Khulna 2.12 1.37–3.25 0.001 Mymensingh 1.01 0.66–1.55 0.964 Rajshahi 1.67 1.08–2.57 0.020 Rangpur 0.92 0.61–1.40 0.704 Sylhet 0.52 0.33–0.80 0.003 Multilevel mixed model for second or higher birth order In the multilevel mixed models for second or higher birth orders, the fully adjusted model also showed better fit for the CS factor in Bangladesh, with the lowest ICC and AIC values, which decreased consistently from the individual to the community level (Table 3 ). For second- or higher-order births, mothers' educational attainment and ANC visits were significant determinants of CS at the individual level. Although education was found to be insignificant for first birth order, mothers with higher education had 3.69 times higher likelihood of CS in second or higher birth order than mothers with no education. Mothers with ANC visits of more than four also revealed a significant contribution, yielding 7.27 times higher odds of CS in second or higher birth order than no ANC visit. Mass media exposure and wealth index were both significant predictors of CS in second- or higher-order births. Mothers from middle- and high-income households had higher odds of CS than mothers from low-income families. Similar results were found for households with media exposure, with a 39% higher likelihood of CS in second- or higher-order births than in households with no mass media exposure. Although place of residence was an insignificant predictor of CS in the first birth, it was significant in second- or higher-order births, with a 27% higher likelihood of CS in urban areas than in rural areas. For second or higher birth orders, regional disparities in CS were also observed in the fully adjusted model, as was observed for first births, with mothers in the Chattogram and Sylhet divisions having lower odds of CS than those in Dhaka. Table 3 Multilevel Mixed model for second or higher birth order caesarean section Individual level Household level Community level Predictors Levels Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p Age of mother at birth (Ref: 15–19 years) 20–24 0.98 0.61–1.58 0.941 0.94 0.58–1.51 0.789 0.95 0.59–1.52 0.818 25–29 1.10 0.68–1.76 0.697 1.01 0.63–1.62 0.967 1.04 0.65–1.67 0.857 30+ 1.15 0.72–1.85 0.557 1.00 0.62–1.60 0.987 1.00 0.62–1.60 0.995 Education of mothers (Ref: No education) Primary 1.12 0.74–1.70 0.593 0.96 0.63–1.46 0.849 0.97 0.64–1.47 0.877 Secondary 2.34 1.56–3.50 < 0.001 1.60 1.06–2.41 0.024 1.55 1.03–2.34 0.034 Higher 7.61 4.79–12.09 < 0.001 4.04 2.52–6.48 < 0.001 3.69 2.31–5.91 < 0.001 Size of the baby at birth (Ref: Larger than average) Average 1.02 0.75–1.39 0.894 1.04 0.77–1.41 0.790 1.07 0.79–1.44 0.677 Smaller than average 1.19 0.82–1.73 0.355 1.31 0.90–1.91 0.155 1.39 0.96–2.02 0.083 Antenatal care visit (Ref: 0) 1–3 4.96 3.18–7.75 < 0.001 4.07 2.61–6.34 < 0.001 3.92 2.52–6.09 < 0.001 4+ 11.15 7.06–17.61 < 0.001 7.71 4.89–12.18 < 0.001 7.27 4.62–11.46 < 0.001 Mass media exposure (Ref: No) Yes 1.51 1.25–1.83 < 0.001 1.39 1.15–1.69 0.001 Wealth index (Ref: Poor) Middle 1.61 1.25–2.06 < 0.001 1.59 1.24–2.03 < 0.001 Rich 2.77 2.21–3.48 < 0.001 2.68 2.11–3.40 < 0.001 Place of residence (Ref: Rural) Urban 1.27 1.01–1.60 0.039 Division (Ref: Dhaka) Barishal 0.93 0.62–1.39 0.725 Chattogram 0.42 0.29–0.60 < 0.001 Khulna 1.83 1.23–2.70 0.003 Mymensingh 0.74 0.50–1.11 0.143 Rajshahi 1.01 0.68–1.51 0.949 Rangpur 0.92 0.62–1.36 0.669 Sylhet 0.34 0.23–0.53 < 0.001 From individual to community level model, ICC (intraclass correlation coefficient), and AIC (Akaike Information Criterion) decreased steadily, suggesting that adjusting for household and community level predictors in the model meaningfully improved the model’s explanatory power by reducing between-cluster heterogeneity (Table 4 ). The model after adding community level predictors revealed the lowest AIC, yielding more robust representation of factors associated with CS in the first birth order. Table 4 Model Validation and Comparison Individual level Household level Community level First birth order Observations 1824 1824 1824 ICC 0.10 0.09 0.06 Marginal R 2 / Conditional R 2 0.128 / 0.219 0.143 / 0.222 0.192 / 0.239 AIC 2351.110 2336.988 2291.049 Second or higher birth order Observations 3091 3091 3091 ICC 0.20 0.16 0.11 Marginal R 2 / Conditional R 2 0.217 / 0.373 0.271 / 0.387 0.330 / 0.401 AIC 3579.831 3474.248 3399.108 Discussion The study aimed to explain the factors that determine the use of caesarean section (CS) in Bangladesh, and more specifically, how birth order can determine the delivery decision. The findings showed a statistically significant difference in CS rates between first-time mothers and those with subsequent births, with a higher prevalence of CS being observed in first-time mothers (52.4%) compared to those with second or higher-order births. This birth-order difference implies that various maternal, household, and community-level variables have a moderating effect on CS decision that varies across birth orders. This study revealed various factors associated with CS decisions that vary across birth orders. Mothers’ age emerged as a salient factor, with older mothers (aged 30+) having substantially more odds of experiencing CS during the first and subsequent births. This aligns with existing literature, which suggests that women aged 30 and above, especially those 35 and older, have significantly higher odds of undergoing CS to avoid increased obstetric risks [ 37 , 38 ]. Similarly, higher educational level correlated with a higher likelihood of CS, especially in second or subsequent pregnancies; however, education was not significant among first-time mothers after adjusting for household and community-level variables. This result suggests that though education has a direct effect on healthcare decisions of multiparous women, other conditions, including access to healthcare and socioeconomic status, may have a greater impact in the primiparous setting. Multiple studies across diverse settings (Indonesia, Nepal, Ghana, DRC, Tanzania, Vietnam) consistently show that women with higher education have significantly higher odds of CS, particularly in second or subsequent pregnancies [ 39 – 43 ]. The size of the baby at birth played a significant role in determining CS in the first birth order, with smaller-than-average babies having lower CS rates than larger-than-average babies. This result aligns with an existing study suggesting that babies with higher birth weight or classified as LGA (typically > 90th percentile or > 4000g) are at substantially increased risk of CS in first-time mothers [ 44 – 46 ]. Mothers with antenatal care visits of more than 4, were found to be almost 7 times higher than CS for both first and subsequent births. This finding highlights the importance of antenatal care in screening and treating pregnancy risks, with the aim that more frequent visits encourage clinical surveillance and increase the likelihood of CS. Studies in India and Rwanda show that women with more than four ANC visits have higher odds of CS, but the increase is typically around 2 to 2.5 times, not sevenfold. For example, in India, women with 4 + ANC visits had an adjusted odds ratio (OR) of 2.28 for CS compared to those with no ANC visits [ 47 ]. In Rwanda, attending at least four ANC visits was also associated with higher CS rates, but not to the extent of a sevenfold increase [ 48 ]. At the household level, wealth index, mass media exposure, and urban residence are strong predictors of higher CS rates, with notable regional differences. One study from Bangladesh revealed that the richest mothers had CS rates up to 41%, compared to 8.7% among the poorest [ 32 , 49 ]. In India, the odds of CS for the richest quintile were nearly eight times higher than for the poorest (OR: 7.87) [ 47 ]. Existing studies also found that mass media exposure and urban residence are robust predictors of higher CS rates [ 49 – 51 ]. The rise in CS rates in Bangladesh, particularly among urban and wealthy families, reflects trends observed in other low- and middle-income countries (LMICs). The comparative analysis of South Asia has also revealed that wealth index, education, and healthcare access are major drivers of higher cesarean section (CS) rates among women in South Asia [ 47 , 52 – 55 ]. This study has several limitations. First, the BDHS 2022 data is cross-sectional, which restricts the establishment of a cause-and-effect relationship between the identified determinants and CS outcomes. Also, the sample is limited to women who have delivered in the most recent year, which may not reflect long-term trends or the cumulative influence of multiple pregnancies on CS decisions. Although healthcare providers' practices or individual preferences may also play a role in differences in CS rates, they were not included in this study. The results of the research study have significant policy and practice implications. With CS rates steadily increasing, particularly in private healthcare, policymakers should encourage the use of evidence-based practices to prevent unnecessary CS. There should be efforts to improve access to quality antenatal care, especially for rural women and those from disadvantaged socioeconomic groups. It can also be done through public health campaigns that raise awareness of the risks of unnecessary CS and support women in making informed choices about their childbirth practices. The intricate relationships among individual, household, and community-based factors influencing CS decisions need to be further examined in future studies. Causal pathways for these factors and their effects on CS outcomes require longitudinal studies to investigate them. Also, qualitative research might illuminate women's experiences and decision-making processes regarding CS, with reference to their past childbirth experiences and the impact of caregivers. The role of healthcare providers' practices, such as advice and recommendations on CS rates, is also a field of interest for further research. Conclusion The current study provides a detailed analysis of the determinants of CS in Bangladesh, highlighting significant differences by birth order, maternal age, education level, antenatal care use, and wealth index. The use of multilevel regression models enabled the identification of individual- and situational-level factors that promote the rise in CS prevalence in the country. The results suggest the need for specific interventions to address socio-economic and regional inequities in CS, meaning that the medically recommended interventions should receive priority, and unnecessary CS should be limited. These insights play a central role in developing policies that would create a fairer and more appropriate maternal healthcare system in Bangladesh. Abbreviations BDHS Bangladesh Demographic and Health Survey CS Caesarean Section ANC Antenatal care Declarations Ethical approval and consent to participate This study is conducted on publicly available secondary Bangladesh Demographic and Health Survey 2022 data; hence no approval was required from any institutional review board as there is no question of human subject protection arises in this case. All methods were performed from the relevant guidelines from the demographic and health survey (DHS) program. Procedures of DHS survey was reviewed by ICF Institutional Review Board (IRB). Competing interests The authors have declared no competing interests. Funding The authors did not received fund from any sources to conduct the research. Author Contribution SC: Original idea of the study, conceptualization, data curation, review, editing. ARA: Data curation, methodology, formal analysis. MAH: Supervision, Critical evaluation of the final draft, validation. All authors have read and approved the final manuscript. Acknowledgement The authors are thankful to Demographic and Health Survey (DHS) program for providing us with BDHS 2022 data. We are also thankful to all the respondents who contributed directly or indirectly to the study. Availability of data and materials The dataset supporting the conclusions of this study is available in the DHS program repository, https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0 . References Veparala AS, et al. Health system drivers of caesarean deliveries in south Asia: a scoping review. The Lancet Regional Health-Southeast Asia; 2025. p. 40. Boerma T, et al. Global epidemiology of use of and disparities in caesarean sections. Lancet. 2018;392(10155):1341–8. Betrán AP, et al. WHO statement on caesarean section rates. BJOG. 2015;123(5):667. Haider M et al. Ever-increasing Caesarean section and its economic burden in Bangladesh. PLoS ONE, 2018. 13. Khan MN et al. Too many yet too few caesarean section deliveries in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys data. PLOS Global Public Health, 2022. 2. Ara A et al. Reducing unnecessary caesarean sections: an action research from a semi-urban hospital in Dhaka, Bangladesh. 2025. Sizear M, Rashid M. Urgent need to address increasing caesarean section rates in lower-middle-income countries like Bangladesh. Frontiers in Global Women's Health; 2024. p. 5. Ahmed MS, et al. Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a nationally representative survey. Int Health. 2022;15(1):30–6. Hossain MS, et al. Cesarean delivery and its determining factors: A hospital-based study in Jashore District, Bangladesh. Public Health Pract. 2024;8:100558. Sujon MSH, et al. Determinants of cesarean section in urban areas of Bangladesh: Insights from the Bangladesh Demographic and Health Survey-2022. Women's Health. 2025;21:17455057251356806. Hossain MA, et al. Obstetric and pregnancy-related factors associated with caesarean delivery in Bangladesh: a survey in Rajshahi district. BMJ open. 2025;15(1):e087668. Elci OC, Edmonson SB, Juusela A. Maternal Morbidity and Mortality from a Population Health Perspective , in Labor and Delivery from a Public Health Perspective . IntechOpen; 2025. Mahada T, Tshitangano TG, Mudau AG. Strategies to reduce maternal death rate and improve the provision of quality healthcare services in selected hospitals of Vhembe District Limpopo Province. Nurs Rep. 2023;13(3):1251–70. Sitaula S et al. Prevalence and risk factors for maternal mortality at a tertiary care centre in Eastern Nepal- retrospective cross sectional study. BMC Pregnancy Childbirth, 2021. 21. Kurjak A, Stanojevic M, Dudenhausen J. Why maternal mortality in the world remains tragedy in low-income countries and shame for high-income ones: will sustainable development goals (SDG) help? J Perinat Med. 2022;51:170–81. Begum T et al. Indications and determinants of caesarean section delivery: Evidence from a population-based study in Matlab, Bangladesh. PLoS ONE, 2017. 12. Faruk M, Arafat ME, Shanta SH. Socioeconomic, demographic, and cultural determinants of delivery by caesarian section in Ethiopia: Evidence from Ethiopia Mini Demographic and Health Survey 2019. PLOS ONE; 2023. p. 18. Coates D, et al. What are women's mode of birth preferences and why? A systematic scoping review. Women and birth: journal of the Australian College of Midwives; 2020. Colomar M et al. Do women prefer caesarean sections? A qualitative evidence synthesis of their views and experiences. PLoS ONE, 2021. 16. Dias M et al. Factors associated with cesarean delivery during labor in primiparous women assisted in the Brazilian Public Health System: data from a National Survey. Reproductive Health, 2016. 13. Begum T et al. Monitoring caesarean births using the Robson ten group classification system: A cross-sectional survey of private for-profit facilities in urban Bangladesh. PLoS ONE, 2019. 14. Hasan F, Alam MM, Hossain MG. Associated factors and their individual contributions to caesarean delivery among married women in Bangladesh: analysis of Bangladesh demographic and health survey data. BMC Pregnancy Childbirth, 2019. 19. Rahman MM et al. Determinants of caesarean section in Bangladesh: Cross-sectional analysis of Bangladesh Demographic and Health Survey 2014 Data. PLoS ONE, 2018. 13. Ahmed M, et al. Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a nationally representative survey. Int Health. 2022;15:30–6. Ireen S, et al. Determinants of Caesarean Deliveries in Bangladesh: A Multilevel Analysis of a Nationwide Population-Based Survey. Jagannath University Journal of Science; 2024. Khan M et al. Trends, District-Level Variations, and Socioeconomic Disparities in Cesarean Section Delivery and its Association with Neonatal Mortality in Bangladesh. 2024. BDHS, Bangladesh Demographic and Health Survey . 2022. Gyaase D, et al. Prevalence and determinants of caesarean section deliveries in the Kintampo Districts of Ghana. BMC Pregnancy Childbirth. 2023;23(1):286. Hasan F, Alam MM, Hossain MG. Associated factors and their individual contributions to caesarean delivery among married women in Bangladesh: analysis of Bangladesh demographic and health survey data. BMC Pregnancy Childbirth. 2019;19(1):433. Karmakar G, et al. Region-specific variation and determinants of caesarean delivery among ever-married women in Bangladesh. PLoS ONE. 2025;20(9):e0328830. Rahman MM, et al. Determinants of caesarean section in Bangladesh: Cross-sectional analysis of Bangladesh Demographic and Health Survey 2014 Data. PLoS ONE. 2018;13(9):e0202879. Kumar P, Sharma H. Prevalence and determinants of socioeconomic inequality in caesarean section deliveries in Bangladesh: an analysis of cross-sectional data from Bangladesh Demographic Health Survey , 2017-18. BMC Pregnancy and Childbirth, 2023. 23. Khan MN, et al. Trends, district-level variations, and socioeconomic disparities in cesarean section delivery in Bangladesh. PLoS ONE. 2025;20(10):e0334931. Ahmed MS, et al. Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a nationally representative survey. Int Health. 2023;15(1):30–6. Austin PC, Merlo J. Intermediate and advanced topics in multilevel logistic regression analysis. Stat Med. 2017;36(20):3257–77. McGillycuddy M, et al. Parsimoniously fitting large multivariate random effects in glmmTMB. J Stat Softw. 2025;112:1–19. Rydahl E et al. Cesarean section on a rise—Does advanced maternal age explain the increase? A population register-based study. PLoS ONE, 2019. 14. Mforteh AAA et al. Maternal and neonatal outcomes at delivery in nulliparous women with advanced maternal age. BMC Pregnancy Childbirth, 2025. 25. Yunitawati D, et al. A Higher Maternal Education Level Could Be a Critical Factor in the Exceeded Cesarean Section Delivery in Indonesia. Iran J Public Health. 2024;53:219–27. Tilahun WM et al. Caesarean section delivery and its associated factors in Ghana: A multilevel analysis. PLoS ONE, 2025. 20. Nahayo B et al. Prevalence and factors associated with caesarean section among Tanzanian women of reproductive age: evidence from the 2022 Tanzania demographic and health survey data. BMC Public Health, 2025. 25. Pandit S et al. Prevalence and associated factors of caesarean section delivery: analysis from the Nepal Demographic and Health Survey 2022. BMJ Open, 2025. 15. De Loenzien M, Mac QNH, Dumont A. Women’s empowerment and elective cesarean section for a single pregnancy: a population-based and multivariate study in Vietnam. BMC Pregnancy Childbirth, 2021. 21. Nedberg I, et al. Factors Associated with Cesarean Section among Primiparous Women in Georgia: A Registry-based Study. J Epidemiol Global Health. 2020;10:337–43. Kuo C-H et al. Re-evaluating large for gestational age: differential effects on perinatal outcomes in term and premature births. Front Med, 2025. 11. Blankenship S, et al. First Stage of Labor Progression in Women with Large-for-Gestational Age Infants. American journal of obstetrics and gynecology; 2019. Pandey A et al. Alarming Trends of Cesarean Section—Time to Rethink: Evidence From a Large-Scale Cross-sectional Sample Survey in India. J Med Internet Res, 2022. 25. Kibe P, et al. Prevalence and factors associated with caesarean section in Rwanda: a trend analysis of Rwanda demographic and health survey 2000 to 2019–20. BMC Pregnancy and Childbirth; 2021. p. 22. Ahmmed F, Manik M, Hossain MJ. Caesarian section (CS) delivery in Bangladesh: A nationally representative cross-sectional study. PLoS ONE, 2021. 16. Sujon MSH, et al. Determinants of cesarean section in urban areas of Bangladesh: Insights from the Bangladesh Demographic and Health Survey-2022. Women's Health; 2025. p. 21. Tp S. Regional Disparities and Determinants of Caesarean Deliveries in India. Indian Journal of Youth & Adolescent Health; 2021. Kundu S et al. Socioeconomic and geographical inequalities in delivery by cesarean section among women in Bangladesh, 2004–2017. BMC Pregnancy Childbirth, 2024. 24. Rana MS, et al. Trends and determinants of caesarean section in South Asian countries: Bangladesh, Nepal, and Pakistan. PLOS ONE; 2024. p. 19. Mumtaz S, Bahk J, Khang Y. Rising trends and inequalities in cesarean section rates in Pakistan: Evidence from Pakistan Demographic and Health Surveys, 1990–2013. PLoS ONE, 2017. 12. Acharya K, Paudel Y. Trend and Sociodemographic Correlates of Cesarean Section Utilization in Nepal: Evidence from Demographic and Health Surveys 2006–2016. BioMed Research International, 2021. 2021. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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This study addresses the gap and aims to contribute to the literature by discovering the factors associated with CS and assessing how these determinants vary by the birth order of the child.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe caesarean section (CS) is an operation in cases where medical conditions like obstruction of labor or fetal distress occur. In recent decades, the number of CS has been growing rapidly in most countries, often without medical justification. This trend has the potential to heighten the risk of maternal and neonatal health, overstretch health resources, and place significant financial pressure on families and health systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Data analysis of 169 countries suggests that approximately 21 per cent of all births worldwide are currently C-section-delivered, amounting to almost 30\u0026nbsp;million caesarean births each year, and the percentage of this group may rise to nearly 28–29 per cent by 2030, assuming current trends continue [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These levels substantially exceed the 10–15% range that the World Health Organization (WHO) has historically regarded as the optimum population-level rate compatible with improved maternal and perinatal outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In low- and middle-income countries (LMICs), including those in South Asia, health systems now face a dual challenge, with some women who genuinely need CS still not getting it.\u003c/p\u003e \u003cp\u003eIn contrast, others undergo CS primarily for non-clinical reasons [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Bangladesh reflects this mixed picture. National surveys and hospital-based studies show that CS use has risen sharply in recent years, with exceptionally high rates in private facilities and urban settings. Caesarean section (CS) rates in Bangladesh have increased dramatically, from about 3% in 2000 to over 33% by 2017/18, with recent estimates in some urban and private facilities exceeding 50–80% [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Numerous studies in the Bangladesh and similar LMICs have investigated individual, as well as community-based determinants of C-section application, such as maternal age, education, household wealth, parity, use of antenatal care, and place or sector of delivery. Multilevel and other modelling techniques have reported strong links of C-section to maternal age, greater socioeconomic status, increased frequency of antenatal visits, and birth in private institutions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In a study conducted in a hospital in the Jashore District, a significant proportion of births was delivered by CS and revealed the maternal age, education, use of the antenatal care (ANC), and delivery in a privately owned facility as significant predictors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The findings can be added to the broader evidence that in Bangladesh, women in richer families have a higher likelihood of having CS, with higher education levels and more frequent visits to ANC indicating that social and health-system factors have a significant impact on mode of delivery [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In 2020, per day about 800 and annually 287000 women lost their live during and following pregnancy and childbirth [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The lifetime risk of dying from pregnancy and childbirth related cause is estimated to be greater in developing countries compared to developed countries as developing countries account for 94–99% of all maternal deaths globally [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough there is already significant research on the determinants of CS, a severe gap remains: the impact of birth order has been explored only as a simple covariate. In the majority of investigations, all births are treated as a homogeneous group, and a single common model is estimated; this method assumes that the determinants of CS are the same for first and subsequent births [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there is some evidence that the mode-of-delivery depends on the decision-making process[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], clinical indications [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and social expectations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] are very different. Primiparous (first-time mothers) women can be more vulnerable to defensive medical practice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], fear of labor problems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], or provider advice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. On the contrary, higher-order births among women introduce previous birth experiences [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], past CS history [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], cumulative maternal morbidity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and changing fertility intentions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which may shape their preferences as well as clinical manifestation. Such parity-specific processes are especially relevant in Bangladesh, where fertility has fallen, childbearing is early and good social value is placed on successful first births. Determining the variation in CS use by birth order and the interactions of this variation with other socioeconomic, demographic, and health-system factors is essential for identifying medically necessary procedures and those that may be avoided.\u003c/p\u003e \u003cp\u003eNevertheless, the available literature in Bangladesh sheds little light on these processes, leaving the drivers of increasing parity-specific CS rates unexplored. Analytically, data on reproduction and healthcare in Bangladesh are stratified; births are contained within mothers, mothers within households, and households within communities. A multilevel regression model thus provides a good fit for identifying individual and contextual determinants of CS [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This will enable the researcher not only to evaluate the maternal attributes, including age, education, wealth, ANC use, and obstetric history, but also the more advanced factors, such as region, community socioeconomic status, and the influence of the local environment of the public-private facility [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. With the introduction of random effects, multilevel models can measure the degree to which the context of communities or facilities, not observable at the individual level, contributes to CS use, not just individuals. To fill the above-identified gaps, the present study will use a birth-order-stratified multilevel modelling approach to test the determinants of CS in Bangladesh. We do not use a single pooled model; instead, we estimate individual first-, second-, and higher-order births. This design enables key predictors to vary by parity, providing more insight into how socioeconomic and health-system factors act differently across birth orders. The joint use of birth-order-specific analysis and multilevel modelling has not been studied before in Bangladesh to determine the CS determinants. This new paradigm offers a more subtle perspective on the interaction among individual factors, situational forces, and parity-specific forces that shape CS use. These insights will be essential for developing specific interventions to enhance medically appropriate, equitable, and cost-effective delivery of care in Bangladesh.\u003c/p\u003e"},{"header":"Methods of the study","content":"\u003cp\u003e \u003cb\u003e2.1 Data source and sample\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThis study used nationally representative Bangladesh Demographic and Health Survey (BDHS) 2022 to identify the determinants of CS in Bangladesh by birth order. The BDHS employed a two-stage stratified sampling technique to ensure representativeness at the national and divisional levels [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The IR dataset of the BDHS 2022 was utilized to collect the variables of the study. Initially, the dataset had 30,078 women respondents. After deleting the missing values on important variables, 4915 weighted observations remained for the analysis. Out of which, 1824 observations were used to find out the determinants of CS for first birth order, and 3091 observations were used to extract the determinants of CS for second or higher birth orders. As caesarean section differs significantly between primiparous and multiparous women, this study fitted model separately for respondents of each parity. This stratified approach is consistent with prior evidence that birth order is a major determinant and modifier of obstetric risk [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e–\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.2 Variables of the Study\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe outcome variable of the study was whether the respondent had Caesarean section (CS) in her last birth. If she had CS in her last birth, she was coded as 1 and if not, she was coded as 0. The main group variable of the study was the birth order of the last birth of the respondent. The original data was separated into two datasets on the basis of first birth order and second or higher birth orders. An extensive literature review was conducted to identify the relevant explanatory variables for the study. The predictor variables included in the study were, age of mother at birth, educational attainment of mother, size of the baby at birth, antenatal care visit, mass media exposure, wealth index of the household, place of residence, and administrative division of residence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.3 Statistical analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eDescriptive statistics were conducted to summarize the socio-demographic characteristics of the respondents. Multilevel mixed effect logistic regression was fitted later on the basis of birth order to identify the determinants of CS in Bangladesh [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Multilevel analysis is an appropriate statistical method for research designs in which participant data is structured across multiple levels. Multilevel logistic regression was applied to adequately handle the hierarchical structure of BDHS data, where the observations were nested within households, which were nested within regions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The problem of dependencies between individual observations often comes from several levels of hierarchy. The dependence also occurs in survey research like BDHS, where the sample was not taken randomly, but cluster sampling from geographical areas was used instead. Traditional regression models assume independence of observations, which would be appropriate for survey research. Hence, this study utilized multilevel regression model to identify the determinants of CS in Bangladesh, which allowed us to control for the potential correlation of individuals within higher level units. In this study, explanatory variables were summarized in three levels: individual level, household level, and community level. The levels were comprised of individual level: Age of mother, education of mothers, size of the baby at birth, antenatal care visits, household level: Mass media exposure, wealth index of the household, and community level: Place of residence, division. In this study, multilevel mixed effect logistic regression was utilized to determine the factors responsible for CS in Bangladesh, as the dataset was multistage cluster survey.\u003c/p\u003e\u003cp\u003eLet, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003ebe the outcome variable measured on the ith subject within jth cluster (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e=1, if the respondent had CS in her last birth, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e=0, if she did not). Furthermore, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1ij},\\:\\dots\\:,\\:{X}_{kij}\\:\\)\u003c/span\u003e\u003c/span\u003eare k independent variables measured on different levels. Then the mixed effect model is,\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left(\\frac{{\\pi\\:}_{ij}\\:}{1-{\\pi\\:}_{ij}}\\right)=\\:{\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1ij}+\\:\\dots\\:+\\:{\\beta\\:}_{k}{X}_{kij}+{u}_{oj}+{e}_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\pi\\:}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003eis the likelihood of occurring CS in the last birth, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1,\\:}\\dots\\:,\\:{\\beta\\:}_{k}\\)\u003c/span\u003e\u003c/span\u003eare the effect sizes of individual and community levels, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{oj}\\)\u003c/span\u003e\u003c/span\u003e are the random errors at cluster levels, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the random errors at the individual levels [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. All statistical analyses were conducted at R 4.5.2. The ‘glmmTMB’ package was utilized to fit the mixed effect logistic regression model [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Variance inflation factor (VIF) was checked for multicollinearity, and it was found that all of the explanatory variable had VIF \u0026lt; 2.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics and bivariate association\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive characteristics of the respondents. The prevalence of Caesarean section was found to be 45.5%. According to the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the prevalence of CS was found higher in first birth order than the second or higher birth orders (52.4% vs 41.4%), which revealed significant difference in CS on the basis of birth order of the children (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the CS was found to be more frequent among mothers with higher educational attainment (70.2%) than among those with lower educational attainment. A higher prevalence of CS was also observed among mothers with more than 4 ANC visits, mass media exposure, a wealthy household, and urban residence in Dhaka, Khulna, and Rajshahi divisions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive characteristics and caesarean section among mothers\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\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBirth order of the\u003c/p\u003e \u003cp\u003elast birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e869\u003c/p\u003e \u003cp\u003e47.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e955\u003c/p\u003e \u003cp\u003e52.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1824\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1810\u003c/p\u003e \u003cp\u003e58.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1281\u003c/p\u003e \u003cp\u003e41.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3091\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2679\u003c/p\u003e \u003cp\u003e54.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2236\u003c/p\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4915\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge of mother\u003c/p\u003e \u003cp\u003eat birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e547\u003c/p\u003e \u003cp\u003e57.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410\u003c/p\u003e \u003cp\u003e42.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e957\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e878\u003c/p\u003e \u003cp\u003e53.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e767\u003c/p\u003e \u003cp\u003e46.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1645\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e673\u003c/p\u003e \u003cp\u003e53.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e598\u003c/p\u003e \u003cp\u003e47.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1271\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e581\u003c/p\u003e \u003cp\u003e55.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461\u003c/p\u003e \u003cp\u003e44.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1042\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation of mothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003cp\u003e77.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e808\u003c/p\u003e \u003cp\u003e71.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319\u003c/p\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1127\u003c/p\u003e \u003cp\u003e100.0%\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\u003e1391\u003c/p\u003e \u003cp\u003e53.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1198\u003c/p\u003e \u003cp\u003e46.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2589\u003c/p\u003e \u003cp\u003e100.0%\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\u003e280\u003c/p\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e661\u003c/p\u003e \u003cp\u003e70.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e941\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSize of child\u003c/p\u003e \u003cp\u003eat birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarger than average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003cp\u003e52.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231\u003c/p\u003e \u003cp\u003e48.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e481\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2055\u003c/p\u003e \u003cp\u003e54.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1723\u003c/p\u003e \u003cp\u003e45.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3778\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmaller than average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e374\u003c/p\u003e \u003cp\u003e57.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282\u003c/p\u003e \u003cp\u003e43.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e656\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAntenatal Care Visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e348\u003c/p\u003e \u003cp\u003e90.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e9.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e386\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1541\u003c/p\u003e \u003cp\u003e61.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e975\u003c/p\u003e \u003cp\u003e38.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2516\u003c/p\u003e \u003cp\u003e100.0%\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\u003e790\u003c/p\u003e \u003cp\u003e39.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1223\u003c/p\u003e \u003cp\u003e60.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMass Media Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1413\u003c/p\u003e \u003cp\u003e66.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e716\u003c/p\u003e \u003cp\u003e33.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2129\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1266\u003c/p\u003e \u003cp\u003e45.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1520\u003c/p\u003e \u003cp\u003e54.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2786\u003c/p\u003e \u003cp\u003e100.0%\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e1410\u003c/p\u003e \u003cp\u003e70.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e594\u003c/p\u003e \u003cp\u003e29.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e542\u003c/p\u003e \u003cp\u003e55.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e440\u003c/p\u003e \u003cp\u003e44.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e982\u003c/p\u003e \u003cp\u003e100.0%\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\u003e727\u003c/p\u003e \u003cp\u003e37.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1202\u003c/p\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1929\u003c/p\u003e \u003cp\u003e100.0%\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1952\u003c/p\u003e \u003cp\u003e59.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1346\u003c/p\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3298\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e727\u003c/p\u003e \u003cp\u003e45.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e890\u003c/p\u003e \u003cp\u003e55.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1617\u003c/p\u003e \u003cp\u003e100.0%\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\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337\u003c/p\u003e \u003cp\u003e46.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389\u003c/p\u003e \u003cp\u003e53.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e726\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarishal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003cp\u003e55.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239\u003c/p\u003e \u003cp\u003e44.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e537\u003c/p\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChattogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e560\u003c/p\u003e \u003cp\u003e66.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289\u003c/p\u003e \u003cp\u003e34.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e849\u003c/p\u003e \u003cp\u003e100.0%\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\u003e187\u003c/p\u003e \u003cp\u003e33.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369\u003c/p\u003e \u003cp\u003e66.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e556\u003c/p\u003e \u003cp\u003e100.0%\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\u003e355\u003c/p\u003e \u003cp\u003e58.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252\u003c/p\u003e \u003cp\u003e41.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e607\u003c/p\u003e \u003cp\u003e100.0%\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\u003e219\u003c/p\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280\u003c/p\u003e \u003cp\u003e56.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e499\u003c/p\u003e \u003cp\u003e100.0%\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\u003e318\u003c/p\u003e \u003cp\u003e55.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251\u003c/p\u003e \u003cp\u003e44.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e569\u003c/p\u003e \u003cp\u003e100.0%\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\u003e405\u003c/p\u003e \u003cp\u003e70.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003cp\u003e29.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e572\u003c/p\u003e \u003cp\u003e100.0%\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\n\u003ch3\u003eMultilevel mixed model for first birth order\u003c/h3\u003e\n\u003cp\u003eThree progressively adjusted multilevel mixed effect models were fitted to examine the determinants of CS in mothers of reproductive age in their first birth order (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAge of mothers at birth, higher educational attainment, size of the baby at birth, and antenatal care visits were significant predictors of CS in first birth order from the individual level in the full adjusted model. Age of mother at first birth played a noteworthy role in determining CS, showing increasing trend of odds with the increase of age, with 2.86 times higher CS for 30\u0026thinsp;+\u0026thinsp;aged mothers than mothers aged 15\u0026ndash;19 years. Although higher educational attainment was found to be significantly associated with 2.48 times higher odds of CS than no education at the individual level, it was found to be insignificant after adjusting for household and community level predictors. Average size of the baby at birth revealed a substantially lower probability of CS (AOR: 0.56) in the first birth than in larger than average size of the baby at birth. More than four of the antenatal care (ANC) visits of mothers more than four were also major contributors to CS in first birth, with almost 7 times higher likelihood of CS than mother with no ANC visit. Between the household level predictors media exposure and wealth index, mothers from the rich households had a 56% higher likelihood of CS in first birth than the mothers of poor households. Although mass media exposure was found to be significant with 36% higher odds of CS in first birth order, it became insignificant after adjusting for community-level predictors in the model. However, marked regional disparities in CS were observed for first birth. While mothers from Khulna and Rajshahi divisions had higher odds of CS in the first birth than Dhaka, Chattogram, and Sylhet divisions showed lower odds of CS in the first birth. Nevertheless, once socioeconomic and maternal factors were controlled, the urban-rural difference in CS diminished as place of residence was found to be insignificant in the fully adjusted model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel Mixed model for first birth order caesarean section\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eIndividual level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHousehold level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eCommunity level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge of mother at birth\u003c/p\u003e \u003cp\u003e(Ref: 15\u0026ndash;19 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026ndash;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.15\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.28\u0026ndash;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u0026ndash;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u0026ndash;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.54\u0026ndash;3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40\u0026ndash;5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u0026ndash;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.43\u0026ndash;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.003\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\u003eEducation of mothers\u003c/p\u003e \u003cp\u003e(Ref: No education)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u0026ndash;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.39\u0026ndash;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.795\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\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u0026ndash;3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u0026ndash;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.59\u0026ndash;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.515\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\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u0026ndash;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75\u0026ndash;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSize of the baby at birth\u003c/p\u003e \u003cp\u003e(Ref: Larger than average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.39\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmaller than average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026ndash;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u0026ndash;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.33\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntenatal care visit\u003c/p\u003e \u003cp\u003e(Ref: 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14\u0026ndash;8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92\u0026ndash;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.94\u0026ndash;7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u0026ndash;16.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.34\u0026ndash;13.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.39\u0026ndash;14.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMass media exposure\u003c/p\u003e \u003cp\u003e(Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\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 \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u0026ndash;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.96\u0026ndash;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003cp\u003e(Ref: Poor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\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 \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u0026ndash;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93\u0026ndash;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.142\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\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 \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.18\u0026ndash;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003cp\u003e(Ref: Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eDivision\u003c/p\u003e \u003cp\u003e(Ref: Dhaka)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarishal\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChattogram\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.37\u0026ndash;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.66\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.964\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.08\u0026ndash;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.61\u0026ndash;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.704\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.33\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultilevel mixed model for second or higher birth order\u003c/h3\u003e\n\u003cp\u003eIn the multilevel mixed models for second or higher birth orders, the fully adjusted model also showed better fit for the CS factor in Bangladesh, with the lowest ICC and AIC values, which decreased consistently from the individual to the community level (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor second- or higher-order births, mothers' educational attainment and ANC visits were significant determinants of CS at the individual level. Although education was found to be insignificant for first birth order, mothers with higher education had 3.69 times higher likelihood of CS in second or higher birth order than mothers with no education. Mothers with ANC visits of more than four also revealed a significant contribution, yielding 7.27 times higher odds of CS in second or higher birth order than no ANC visit. Mass media exposure and wealth index were both significant predictors of CS in second- or higher-order births. Mothers from middle- and high-income households had higher odds of CS than mothers from low-income families. Similar results were found for households with media exposure, with a 39% higher likelihood of CS in second- or higher-order births than in households with no mass media exposure. Although place of residence was an insignificant predictor of CS in the first birth, it was significant in second- or higher-order births, with a 27% higher likelihood of CS in urban areas than in rural areas. For second or higher birth orders, regional disparities in CS were also observed in the fully adjusted model, as was observed for first births, with mothers in the Chattogram and Sylhet divisions having lower odds of CS than those in Dhaka.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel Mixed model for second or higher birth order caesarean section\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eIndividual level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHousehold level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eCommunity level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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 of mother at birth\u003c/p\u003e \u003cp\u003e(Ref: 15\u0026ndash;19 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u0026ndash;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.59\u0026ndash;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.65\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.62\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation of mothers\u003c/p\u003e \u003cp\u003e(Ref: No education)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.64\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.877\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\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026ndash;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u0026ndash;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.03\u0026ndash;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\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\u003e7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.79\u0026ndash;12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.52\u0026ndash;6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.31\u0026ndash;5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSize of the baby at birth\u003c/p\u003e \u003cp\u003e(Ref: Larger than average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmaller than average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u0026ndash;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u0026ndash;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntenatal care visit\u003c/p\u003e \u003cp\u003e(Ref: 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.18\u0026ndash;7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.61\u0026ndash;6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.52\u0026ndash;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.06\u0026ndash;17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.89\u0026ndash;12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.62\u0026ndash;11.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMass media exposure\u003c/p\u003e \u003cp\u003e(Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.15\u0026ndash;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003cp\u003e(Ref: Poor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u0026ndash;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.24\u0026ndash;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.21\u0026ndash;3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.11\u0026ndash;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003cp\u003e(Ref: Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.01\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eDivision\u003c/p\u003e \u003cp\u003e(Ref: Dhaka)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarishal\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.62\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChattogram\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.29\u0026ndash;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.23\u0026ndash;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.50\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.143\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.68\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.949\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.62\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.669\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\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.23\u0026ndash;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eFrom individual to community level model, ICC (intraclass correlation coefficient), and AIC (Akaike Information Criterion) decreased steadily, suggesting that adjusting for household and community level predictors in the model meaningfully improved the model\u0026rsquo;s explanatory power by reducing between-cluster heterogeneity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The model after adding community level predictors revealed the lowest AIC, yielding more robust representation of factors associated with CS in the first birth order.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Validation and Comparison\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndividual level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHousehold level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCommunity level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFirst birth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e / Conditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128 / 0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143 / 0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192 / 0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2351.110\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2336.988\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2291.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSecond or higher birth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e / Conditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.217 / 0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271 / 0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.330 / 0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3579.831\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3474.248\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3399.108\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study aimed to explain the factors that determine the use of caesarean section (CS) in Bangladesh, and more specifically, how birth order can determine the delivery decision. The findings showed a statistically significant difference in CS rates between first-time mothers and those with subsequent births, with a higher prevalence of CS being observed in first-time mothers (52.4%) compared to those with second or higher-order births. This birth-order difference implies that various maternal, household, and community-level variables have a moderating effect on CS decision that varies across birth orders.\u003c/p\u003e \u003cp\u003eThis study revealed various factors associated with CS decisions that vary across birth orders. Mothers\u0026rsquo; age emerged as a salient factor, with older mothers (aged 30+) having substantially more odds of experiencing CS during the first and subsequent births. This aligns with existing literature, which suggests that women aged 30 and above, especially those 35 and older, have significantly higher odds of undergoing CS to avoid increased obstetric risks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, higher educational level correlated with a higher likelihood of CS, especially in second or subsequent pregnancies; however, education was not significant among first-time mothers after adjusting for household and community-level variables. This result suggests that though education has a direct effect on healthcare decisions of multiparous women, other conditions, including access to healthcare and socioeconomic status, may have a greater impact in the primiparous setting. Multiple studies across diverse settings (Indonesia, Nepal, Ghana, DRC, Tanzania, Vietnam) consistently show that women with higher education have significantly higher odds of CS, particularly in second or subsequent pregnancies [\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The size of the baby at birth played a significant role in determining CS in the first birth order, with smaller-than-average babies having lower CS rates than larger-than-average babies. This result aligns with an existing study suggesting that babies with higher birth weight or classified as LGA (typically\u0026thinsp;\u0026gt;\u0026thinsp;90th percentile or \u0026gt;\u0026thinsp;4000g) are at substantially increased risk of CS in first-time mothers [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Mothers with antenatal care visits of more than 4, were found to be almost 7 times higher than CS for both first and subsequent births. This finding highlights the importance of antenatal care in screening and treating pregnancy risks, with the aim that more frequent visits encourage clinical surveillance and increase the likelihood of CS. Studies in India and Rwanda show that women with more than four ANC visits have higher odds of CS, but the increase is typically around 2 to 2.5 times, not sevenfold. For example, in India, women with 4\u0026thinsp;+\u0026thinsp;ANC visits had an adjusted odds ratio (OR) of 2.28 for CS compared to those with no ANC visits [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In Rwanda, attending at least four ANC visits was also associated with higher CS rates, but not to the extent of a sevenfold increase [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the household level, wealth index, mass media exposure, and urban residence are strong predictors of higher CS rates, with notable regional differences. One study from Bangladesh revealed that the richest mothers had CS rates up to 41%, compared to 8.7% among the poorest [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In India, the odds of CS for the richest quintile were nearly eight times higher than for the poorest (OR: 7.87) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Existing studies also found that mass media exposure and urban residence are robust predictors of higher CS rates [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rise in CS rates in Bangladesh, particularly among urban and wealthy families, reflects trends observed in other low- and middle-income countries (LMICs). The comparative analysis of South Asia has also revealed that wealth index, education, and healthcare access are major drivers of higher cesarean section (CS) rates among women in South Asia [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the BDHS 2022 data is cross-sectional, which restricts the establishment of a cause-and-effect relationship between the identified determinants and CS outcomes. Also, the sample is limited to women who have delivered in the most recent year, which may not reflect long-term trends or the cumulative influence of multiple pregnancies on CS decisions. Although healthcare providers' practices or individual preferences may also play a role in differences in CS rates, they were not included in this study.\u003c/p\u003e \u003cp\u003eThe results of the research study have significant policy and practice implications. With CS rates steadily increasing, particularly in private healthcare, policymakers should encourage the use of evidence-based practices to prevent unnecessary CS. There should be efforts to improve access to quality antenatal care, especially for rural women and those from disadvantaged socioeconomic groups. It can also be done through public health campaigns that raise awareness of the risks of unnecessary CS and support women in making informed choices about their childbirth practices.\u003c/p\u003e \u003cp\u003eThe intricate relationships among individual, household, and community-based factors influencing CS decisions need to be further examined in future studies. Causal pathways for these factors and their effects on CS outcomes require longitudinal studies to investigate them. Also, qualitative research might illuminate women's experiences and decision-making processes regarding CS, with reference to their past childbirth experiences and the impact of caregivers. The role of healthcare providers' practices, such as advice and recommendations on CS rates, is also a field of interest for further research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study provides a detailed analysis of the determinants of CS in Bangladesh, highlighting significant differences by birth order, maternal age, education level, antenatal care use, and wealth index. The use of multilevel regression models enabled the identification of individual- and situational-level factors that promote the rise in CS prevalence in the country. The results suggest the need for specific interventions to address socio-economic and regional inequities in CS, meaning that the medically recommended interventions should receive priority, and unnecessary CS should be limited. These insights play a central role in developing policies that would create a fairer and more appropriate maternal healthcare system in Bangladesh.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\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\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCaesarean Section\u003c/p\u003e \u003c/div\u003e \u003c/div\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 \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval and consent to participate\u003c/h2\u003e \u003cp\u003eThis study is conducted on publicly available secondary Bangladesh Demographic and Health Survey 2022 data; hence no approval was required from any institutional review board as there is no question of human subject protection arises in this case. All methods were performed from the relevant guidelines from the demographic and health survey (DHS) program. Procedures of DHS survey was reviewed by ICF Institutional Review Board (IRB).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have declared no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not received fund from any sources to conduct the research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSC: Original idea of the study, conceptualization, data curation, review, editing. ARA: Data curation, methodology, formal analysis. MAH: Supervision, Critical evaluation of the final draft, validation. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are thankful to Demographic and Health Survey (DHS) program for providing us with BDHS 2022 data. We are also thankful to all the respondents who contributed directly or indirectly to the study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe dataset supporting the conclusions of this study is available in the DHS program repository, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVeparala AS, et al. Health system drivers of caesarean deliveries in south Asia: a scoping review. The Lancet Regional Health-Southeast Asia; 2025. p. 40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoerma T, et al. Global epidemiology of use of and disparities in caesarean sections. Lancet. 2018;392(10155):1341\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBetr\u0026aacute;n AP, et al. WHO statement on caesarean section rates. BJOG. 2015;123(5):667.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaider M et al. Ever-increasing Caesarean section and its economic burden in Bangladesh. PLoS ONE, 2018. 13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan MN et al. Too many yet too few caesarean section deliveries in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys data. PLOS Global Public Health, 2022. 2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAra A et al. \u003cem\u003eReducing unnecessary caesarean sections: an action research from a semi-urban hospital in Dhaka, Bangladesh.\u003c/em\u003e 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSizear M, Rashid M. Urgent need to address increasing caesarean section rates in lower-middle-income countries like Bangladesh. Frontiers in Global Women's Health; 2024. p. 5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed MS, et al. Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a nationally representative survey. Int Health. 2022;15(1):30\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MS, et al. Cesarean delivery and its determining factors: A hospital-based study in Jashore District, Bangladesh. 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J Med Internet Res, 2022. 25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKibe P, et al. Prevalence and factors associated with caesarean section in Rwanda: a trend analysis of Rwanda demographic and health survey 2000 to 2019\u0026ndash;20. BMC Pregnancy and Childbirth; 2021. p. 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmmed F, Manik M, Hossain MJ. Caesarian section (CS) delivery in Bangladesh: A nationally representative cross-sectional study. PLoS ONE, 2021. 16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSujon MSH, et al. Determinants of cesarean section in urban areas of Bangladesh: Insights from the Bangladesh Demographic and Health Survey-2022. Women's Health; 2025. p. 21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTp S. Regional Disparities and Determinants of Caesarean Deliveries in India. Indian Journal of Youth \u0026amp; Adolescent Health; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKundu S et al. Socioeconomic and geographical inequalities in delivery by cesarean section among women in Bangladesh, 2004\u0026ndash;2017. BMC Pregnancy Childbirth, 2024. 24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRana MS, et al. Trends and determinants of caesarean section in South Asian countries: Bangladesh, Nepal, and Pakistan. PLOS ONE; 2024. p. 19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMumtaz S, Bahk J, Khang Y. Rising trends and inequalities in cesarean section rates in Pakistan: Evidence from Pakistan Demographic and Health Surveys, 1990\u0026ndash;2013. PLoS ONE, 2017. 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcharya K, Paudel Y. \u003cem\u003eTrend and Sociodemographic Correlates of Cesarean Section Utilization in Nepal: Evidence from Demographic and Health Surveys 2006\u0026ndash;2016.\u003c/em\u003e BioMed Research International, 2021. 2021.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cesarean section, parity specific, multilevel mixed effect logistic regression, BDHS, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-8350383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8350383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe use of Caesarean section (CS) in Bangladesh has increased significantly above the medically justified levels and it varies by socioeconomic background. However, there is little research on the determinants of CS that differ by parity, although there are solid clinical and social arguments that parity-specific effects are likely. This paper used a multilevel-based investigation to explore birth-order-specific determinants of CS in Bangladesh.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used data from Bangladesh Demographic and Health Survey 2022 to explore the determinants of caesarean section in Bangladesh. The binary outcome variable of the study was whether the respondent had CS in last birth or not. To detect heterogeneity by birth order, the dataset was stratified into two groups; first births and second or higher births. Separate multilevel mixed effect models were then fitted for each group to identify how CS varied by parity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that 45.5% of births were delivered by CS. This rate was higher for first births (52.4%) than for second or higher-order births (41.4%). The community level model performed better for both the stratified models. For first births, age of mothers at birth, higher educational attainment, size of the baby at birth, and antenatal care visits were significant predictors of CS, while rural\u0026ndash;urban disparities were reduced after adjustment. For second or higher-order births, higher maternal education, ANC (\u0026ge;\u0026thinsp;4 visits), exposure to the mass media, richer wealth status, and urban residence were important predictors of CS, with notable regional variations (lower odds in Chattogram and Sylhet compared with Dhaka).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings highlight the need for parity-specific, context-specific strategies to reduce unnecessary CS while ensuring access for women in genuine need, particularly by addressing socioeconomic and regional inequities in maternal healthcare.\u003c/p\u003e","manuscriptTitle":"Birth-order based determinants of caesarean sections in Bangladesh: A multilevel mixed effect regression approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 08:52:25","doi":"10.21203/rs.3.rs-8350383/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05100a56-493d-495c-9667-2108f0ecbd94","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T08:10:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 08:52:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8350383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8350383","identity":"rs-8350383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}