Quantifying Urban–Rural Disparities in Caesarean Section Deliveries in India: A Multilevel and Fairlie Decomposition Approach

Quantifying Urban–Rural Disparities in Caesarean Section Deliveries in India: A Multilevel and Fairlie Decomposition 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 Quantifying Urban–Rural Disparities in Caesarean Section Deliveries in India: A Multilevel and Fairlie Decomposition Approach Priyanka Patel, Milan Das, Harpreet Kour, Mayank Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9229990/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Introduction: Caesarean section (C-section) delivery is a critical maternal health intervention when medically indicated; however, its rapid increase beyond recommended levels (range of 10–15%) raises concerns about unnecessary surgical births. India has witnessed a substantial rise in C-section deliveries, with pronounced urban–rural differentials, reflecting inequalities in healthcare access, service provision, and socioeconomic status. Understanding how place of residence interacts with demographic and socioeconomic factors is essential for addressing inequities in delivery care. Data and Methods: This study used data from the National Family Health Survey (NFHS-5, 2019–21), covering 155,624 most recent institutional births among currently married women aged 15–49 years in India. Descriptive and bivariate analyses were followed by stratified (urban, rural and total) multilevel logistic regression models (individuals nested within PSUs, districts, and states) to assess contextual variation and determinants. Model fit was evaluated using AIC, BIC, and intra-class correlation coefficients (ICCs). Furthermore, to understand the factors associated with differential pattern in Caesarean delivery in rural and urban Fairlie decomposition technique has been applied. Results Overall, 26.6% of deliveries occurred by C-section, with a substantially higher prevalence in urban areas (36.6%) compared to rural areas (22.3%). Multilevel analysis showed significant clustering at PSU, district, and state levels. Deliveries in private hospitals had markedly higher odds of C-section (AOR = 5.02; 95% CI: 4.84–5.20). Higher maternal age, education, wealth status, ≥ 4 ANC visits, and pregnancy complications increased the likelihood of C-section, while higher birth order and longer birth intervals reduced it. Even after adjustment, rural residence remained associated with lower odds of C-section. Fairlie decomposition analysis revealed that 93.8% of the urban–rural gap in caesarean section deliveries is explained by observed characteristics, with maternal factors contributing the largest share. Place of delivery alone accounted for nearly half of the explained disparity (49.9%), followed by household wealth (15.7%) and region (8.3%). Conclusion C-section delivery in India is strongly patterned by place of residence, healthcare sector, and socioeconomic advantage. The findings highlight growing urban–private sector dominance in C-section use and persistent contextual inequalities. Policy efforts should focus on regulating private facilities, improving quality of obstetric care in rural areas, and promoting evidence-based delivery practices to ensure medically appropriate and equitable maternal healthcare. Caesarean section Urban–rural disparities Multilevel logistic regression Maternal health services Private healthcare NFHS-5 India Figures Figure 1 Key Message What is already known on this topic Caesarean section rates have increased in India, with higher prevalence in urban areas and private healthcare facilities. Socioeconomic and healthcare factors contribute to disparities in caesarean delivery. What this study adds Caesarean delivery was substantially higher in urban areas and private hospitals, with private-sector delivery increasing the odds fivefold. Fairlie decomposition showed that 93.8% of the rural–urban gap was explained, with place of delivery contributing nearly half of the disparity. How this study might affect research, practice or policy Findings highlight the need to regulate unnecessary caesarean deliveries in private facilities and improve equitable access to obstetric care. Addressing structural inequalities may help ensure appropriate and evidence-based use of caesarean delivery. Introduction A Caesarean section is a surgical procedure in which the baby is delivered through incisions in the abdominal and uterine walls and is generally recommended when vaginal delivery poses risks to the mother or the fetus (Betrán et al., 2016 ; Gregory et a., 2012). Over time, the use of caesarean delivery has shown a marked upward trend across many countries, reflecting changing obstetric practices and patterns of childbirth. For instance, India experiencing a substantial rise from 3% in 1992–93 to 11% in 2005–06, and further to 22% in 2019–21, with nearly 30% of all births in India (International Institute for Population Sciences (IIPS) &ICF, 2021; Singh et al., 2022 ). While Caesarean delivery is a crucial life-saving intervention, its rising utilization beyond medically recommended levels raised significant public health concerns. Unnecessary caesarean sections are associated with increased maternal and neonatal morbidity and mortality, higher healthcare costs, and avoidable medical risks, underscoring the need to understand the factors driving this upward trend (Lumbiganon et al., 2010 ; Bhatnagar et al., 2023 ; Govil et al., 2020 ). The World Health Organization (WHO) recommends that CS rates should ideally be between 10% and 15% of all deliveries (Abate et al., 2025 ; Rai et al., 2019 ; Gondwe et al., 2020 ). Although essential in specific obstetric situations but rates exceeding this threshold often suggest non-medically indicated procedures, leading to short- and long-term complications for both mothers and infants (Gondwe et al., 2020 ; Darnal & Dangal, 2020 ). Short-term risks for mothers include hemorrhage, infection, anesthesia-related complications, uterine rupture, renal failure, obstetric shock, cardiac arrest, venous thromboembolism, and severe puerperal infection (Arendt et al., 2020 ). Long-term consequences can involve increased likelihood of bowel obstruction due to pelvic adhesions, subfertility, decreased satisfaction with the birthing experience, shorter breastfeeding duration, and less satisfying mother-child interactions (Ananna & Hossain; 2023 ). For infants, CS delivery influences initial colonization of the gut microbiome, potentially affecting long-term immune system development. Moreover, the increased risk of uterine rupture in subsequent pregnancies after a CS can lead to preterm birth, stillbirth, and maternal death (Sahoo & Jeermison; 2020 ). In low-resource settings, these risks are further amplified due to limited access to quality surgical care and post-operative management. Despite such concerns, caesarean section rates have reached 30–50% in several high- and middle-income countries including Germany, Italy, France, the United States, and Australia (Sufang et al., 2007 ). In several middle-income countries, including Brazil, China, and India, the private healthcare sector plays a substantial role in maternity care, particularly in urban areas, and is often associated with markedly higher caesarean section rates compared with public facilities (Sufang et al., 2007 ; Cai et al., 1998 ; Potter et al., 2001 ). A significant urban–rural disparity in Caesarean section (CS) rates persists in India, with NFHS-5 (2019–21) reporting a national prevalence of nearly 30%, largely driven by substantially higher rates in urban areas, particularly in private facilities where CS prevalence frequently exceeds 40–50%, compared with 15–20% in rural settings (Abate et al., 2025 ; Kumar & Lakhtakia, 2021 ; Oinam et al., 2020 ; Chaurasia, 2022 ). This differential reflects not only variations in obstetric risk but also structural and systemic factors, most notably the greater reliance on private healthcare in urban areas, which consistently report significantly higher CS rates than public facilities (Bhatia et al., 2020 , Sarkar, S. 2020 ; Simmons et al., 2021 ; Sharma et al., 2021 ). Evidence from rural eastern Maharashtra (2010–2017) demonstrates rapidly increasing CS rates concentrated in private institutions, and multiple studies identify delivery in private hospitals as a key determinant of CS use (Sk, R. 2020 ; Simmons et al., 2021 ). These patterns are commonly attributed to financial incentives, defensive medical practices, provider convenience, and maternal preferences, including elective CS without medical indication, which is more prevalent among educated and affluent women in private-sector settings (Bhartia et al., 2020 ; Singh et al., 2020 ). In contrast, rural areas often experience constrained access to skilled birth attendants and comprehensive emergency obstetric care, leading to lower CS rates even when clinically indicated a “too little, too late” scenario while urban private facilities exemplify a “too much, too soon” pattern (Abate et al., 2025 ; Shukla et al., 2022 ). Persistent gaps in institutional delivery coverage and unequal availability of emergency obstetric services, including CS capability and blood transfusion, further reinforce this urban–rural divide (Kumar & Lakhtakia, 2021 ; Mose & Abebe, 2021 ). Caesarean section (CS) delivery in India reflects a pronounced urban–rural divide, characterized by limited access to medically necessary CS in rural areas and excessive, often non-indicated use in urban private facilities, raising concerns about equity, quality of care, and health system efficiency. Rural women frequently face barriers such as inadequate obstetric infrastructure and delayed emergency care, while urban private-sector dominance, weak regulation, and financial incentives contribute to CS overuse. Although regulatory approaches such as clinical audits, fee transparency, evidence-based guidelines, and quality improvement initiatives have shown promise in rationalizing CS use, population-level evidence identifying the drivers of urban–rural disparities remain limited. Therefore, this study is essential to systematically examine urban–rural differentials in CS delivery in India along with the factors contributing this differential and to generate evidence that can inform targeted policies aimed at ensuring equitable access to life-saving obstetric interventions while curbing unnecessary surgical deliveries. Data and Methods The study used data from the National Family Health Survey (NFHS-5) 2019-21, a comprehensive survey conducted in several rounds and includes households all over India. The Ministry of Health and Family Welfare (MoHFW), Government of India, conducts this periodic survey, with the International Institute of Population Sciences (IIPS), Mumbai serving as the survey's main agency. NFHS is a reputable source of information on various socioeconomic, demographic, and health indicators at the national and sub-national levels and is frequently referred to as India's DHS. Such indicators include family planning, maternal and child health service utilization, fertility, mortality, and other pertinent variables. This cross-sectional survey collects the data from all states and union territories of India using standardized questionnaire administered in 18 Indian languages via computer assisted personnel interview (CAPI), employing a two-stage stratified sampling design in rural and urban areas. In the first stage, primary sampling units (villages in rural areas and census enumeration blocks in urban areas) were selected using probability proportional to size, followed by systematic selection of households in the second stage. The child recode file, which forms the basis of this analysis, contains detailed information on all live births along with mode of delivery occurring in the five years preceding each survey, linked to maternal and household characteristics from the corresponding women’s and household files. NFHS-5 includes data on 232,920 children born to women aged 15–49. Data quality was rigorously maintained through uniform training protocols, field supervision, back-checks, and the use of standardized field-check tables (International Institute for Population Sciences (IIPS) &ICF, 2021). Sample Selection Procedure: Figure 1 presents the sample selection process and distribution of caesarean section deliveries by place of residence. Out of 232,920 births reported in the last five years, 56,077 births were excluded as the analysis was restricted to the most recent birth, resulting in a final sample of 176,843 last births. Among these, 155,624 births occurred in health institutions and were included in the analysis. Of the institutional births, 29.65% were from urban areas (N = 35,904) and 70.35% from rural areas (N = 119,720). Caesarean section deliveries were substantially higher in urban areas, with 36.61% of institutional births delivered by caesarean section, compared to 22.33% in rural areas, highlighting a pronounced urban–rural disparity in caesarean section utilization. Variable Description: Outcome variable : The current study focus on the last birth caesarean section and uses a binary outcome variable to assess the mode of delivery for the most recent birth among currently married women aged 15–49 years by the place of residence. The variable specifically represented whether the delivery was via caesarean section and was based on the mother's self-reported data. Given the importance of caesarean deliveries as an indicator of maternal health and healthcare access, their inclusion as the primary outcome variable is noteworthy. The binary nature of the outcome variable, measured using "yes" or "no" responses, made data analysis using appropriate statistical methods easier. Explanatory variables: This study utilized several socio-demographic characteristics as explanatory variables. These included age at marriage, categorized as less than 18 years, 18–24 years, and 25 years or older. Mother's age at childbirth is less than 20 years, 20–29 years, and 30 years or older. Mother's schooling as no education, primary education, secondary education, and higher education. Other variables included pregnancy problems (yes or no), the size of the child (bigger than normal, normal, or less than normal), registration with ANM (yes or no), place of delivery (government hospital or private hospital), birth order and interval (first birth order, second or third birth order with intervals of less than 24 months, second or third birth order with intervals of more than 24 months, fourth or higher birth order with intervals of less than 24 months, and fourth or higher birth order with intervals of more than 24 months), ANC visits (less than or equal to three visits or more than three visits), ever pregnancy termination (no or yes), wealth index (poorest, poorer, middle, richer, and richest), place of residence (rural or urban), religion (Hindu or non-Hindu), social status (SC/ST, OBC, or others), and geographical region (North, Central, East, Northeast, West, or South). Statistical Analysis The study began with a descriptive analysis that determined the responders' frequency distribution and the prevalence of C-section deliveries. The next step was to conduct a bivariate analysis using a chi-square test to find any potential explanatory variables related to C-section delivery. A p -value of < 0.05 or less was regarded as statistically significant. The variance inflation factor (VIF) was used to conduct a multicollinearity test on all significant explanatory factors found in the bivariate analysis to prevent multicollinearity among the explanatory variables. No evidence of collinearity between the explanatory variables was found, according to the results of the VIF test. O'Brien deemed the VIF values to be acceptable when they were less than 10 (O’brien, 2007 ). Model Specification for Multilevel Logistic Regression A multilevel logistic regression model was used to analyse the variation in the prevalence of C-section delivery across different geographic levels. The data used in the analysis had a hierarchical structure consisting of four levels: with individuals at level 1, primary sampling unis (PSUs) at level 2, districts at level 3, and states/union territories at level 4. The probability of caesarean delivery was modelled using a four-level random-intercept logistic regression model specified as: $$\:Logit\:[P\left({Y}_{ijkl}=1\right)=\:{\beta\:}_{0}+\:\:\sum\:_{p=1}^{P}{\beta\:}_{p}{{X}_{pi}}_{jkl}+(\:{u}_{0l}+\:{u}_{0kl}+\:{u}_{0jkl}\:)$$ Where, \(\:{\beta\:}_{0}\) is the overall fixed intercept, representing the log-odds of C-section delivery for women in the reference category of all explanatory variables, \(\:{X}_{pijkl}\) is a vector of individual-level socioeconomic and demographic covariates (e.g., maternal age, parity, education, wealth status, antenatal care utilization, place of residence), \(\:{\beta\:}_{p}\) are the corresponding fixed-effect coefficients, \(\:{u}_{0jkl}\sim\:N(0,{\sigma\:}_{u}^{2})\) represents the random effect at the PSU level, \(\:{v}_{0kl}\sim\:N(0,{\sigma\:}_{v}^{2})\) represents the random effect at the district level, \(\:{f}_{0l}\sim\:N(0,{\sigma\:}_{f}^{2})\) represents the random effect at the state/union territory level. These random intercepts capture unobserved contextual heterogeneity operating at different geographic levels, such as regional health system capacity, provider practices, and policy environments. Two nested multilevel logistic regression models were fitted Model I (Null Model) A model without explanatory variables, including only random intercepts, was estimated to examine the extent of between-cluster variation in C-section delivery and to compute the Intra-Class Correlation Coefficient (ICC). Model II (Adjusted Model) This model included selected socioeconomic and demographic characteristics of the respondents to assess their association with C-section delivery while accounting for clustering at PSU, district, and state levels. The ICC was calculated using the latent variable method for logistic models as: $$\:\text{ICC}=\frac{{\sigma\:}_{u}^{2}+{\sigma\:}_{v}^{2}+{\sigma\:}_{f}^{2}}{{\sigma\:}_{u}^{2}+{\sigma\:}_{v}^{2}+{\sigma\:}_{f}^{2}+\frac{{\pi\:}^{2}}{3}}$$ where \(\:\frac{{\pi\:}^{2}}{3}\) represents the individual-level variance of logistic distribution. All models were estimated using maximum likelihood estimation. Results are presented as adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) (Goldstein, 2011 ; Garson, 2013 ). Fairlie Decomposition analysis To quantify the extent to which differences in observed sociodemographic characteristics explain the rural–urban disparity in caesarean section (C-section) deliveries, we employed the Fairlie Decomposition Analysis (FDA). FDA is a nonlinear decomposition technique suitable for binary outcomes estimated using logit or probit models (Fairlie, 2005 ; Powers et al., 2011 ). This method extends the classical Blinder–Oaxaca decomposition framework developed for linear regression models (Blinder, 1973 ; Oaxaca, 1973 ) which has been widely criticized for its inappropriateness in nonlinear contexts such as logit and probit models due to issues related to model identification and scale dependence (Hlavac, 2014 ), to nonlinear probability models, thereby avoiding problems of identification and scale dependence inherent in applying linear decomposition to logit or probit models (Hlavac, 2014 ). Model Specification for Fairlie Decomposition Let \(\:{Y}_{i}\) denote the binary indicator of caesarean section delivery, coded as Y = 1 if the woman delivered by caesarean section and Y = 0 if the woman delivered by normal (vaginal) delivery. Let \(\:{S}_{i}\) denote place of residence, where \(\:S=1\) indicates urban women and \(\:S=0\) indicates rural women. Let \(\:{X}_{i}\) be a vector of explanatory variables capturing child, maternal, and household characteristics. Predicted probabilities of caesarean section delivery were obtained from a multilevel logistic regression model (Table 3 , Full Model for total column) estimated on the pooled sample: \(\:{\widehat{Y}}_{i}=F\left({X}_{i}\widehat{\beta\:}\right)\) where \(\:F(.)\) is the multilevel logistic cumulative distribution function and \(\:\widehat{\beta\:}\) is the vector of estimated coefficients. The difference in mean predicted probabilities of caesarean section delivery between rural and urban women is given by: \(\:{\Delta\:}={\stackrel{\prime }{Y}}_{1}-{\stackrel{\prime }{Y}}_{0}=\frac{1}{{N}_{1}}\sum\:_{i=1}^{{N}_{1}}F({X}_{1i}\widehat{\beta\:})-\frac{1}{{N}_{0}}\sum\:_{i=1}^{{N}_{0}}F({X}_{0i}\widehat{\beta\:})\) where: \(\:{\stackrel{\prime }{Y}}_{1}\) and \(\:{\stackrel{\prime }{Y}}_{0}\) denote the average predicted probabilities of caesarean section delivery between rural and urban women, respectively; \(\:{N}_{1}\) and \(\:{N}_{0}\) represent the sample sizes of the two groups; \(\:{X}_{1i}\) and \(\:{X}_{0i}\) are covariate vectors for individuals in the respective groups. Following Fairlie ( 2005 ), the total difference in predicted probabilities is decomposed into explained and unexplained components as: $$\:{\stackrel{\prime }{Y}}_{1}-{\stackrel{\prime }{Y}}_{0}=\left[\frac{1}{{N}_{1}}\sum\:_{i=1}^{{N}_{1}}F({X}_{1i}{\widehat{\beta\:}}_{1}\right)-\frac{1}{{N}_{0}}\sum\:_{i=1}^{{N}_{0}}F({X}_{0i}{\widehat{\beta\:}}_{1}\left)\right]+\left[\frac{1}{{N}_{0}}\sum\:_{i=1}^{{N}_{0}}F({X}_{0i}{\widehat{\beta\:}}_{1}\right)-\frac{1}{{N}_{0}}\sum\:_{i=1}^{{N}_{0}}F({X}_{0i}{\widehat{\beta\:}}_{0}\left)\right]$$ Where the first term represents the portion of the caesarean section delivery gap attributable to differences in observed characteristics (explained component); and the second term captures differences due to coefficients and unobserved factors (unexplained component); and \(\:{\widehat{\beta\:}}_{1}\) and \(\:{\widehat{\beta\:}}_{0}\) denote coefficient vectors estimated separately for rural and urban women. The variable-specific contributions to the explained component were obtained using the Fairlie nonlinear decomposition procedure, which estimates the change in average predicted probabilities when the distribution of each explanatory variable is sequentially replaced between groups while holding other variables constant. This procedure is implemented within the decomposition algorithm and does not rely on linear approximations of mean covariate differences (Fairlie, 2005 ). Differences in sample sizes between groups were accounted for within the decomposition routine through matching based on predicted probabilities, as implemented in the statistical software. The explained component reflects the proportion of the caesarean section delivery gap attributable to differences in observable child, maternal, and household characteristics, including maternal age, education, pregnancy related problem, size of child, place of delivery, household wealth, residence, region, and exposure to mass media, while the unexplained component represents differences due to differential effects of these characteristics and unobserved factors. Statistical analysis was conducted using STATA version 17.0, with all estimates derived using appropriate sampling weights to account for the complex survey design of the NFHS. Ethics declaration Ethical clearance for the National Family Health Survey-5 (NFHS-5) was obtained by the International Institute for Population Sciences (IIPS), Mumbai, from its Institutional Ethics Committee, and data collection was conducted in accordance with national and international ethical standards for survey research. The present study uses publicly available, anonymized secondary data from NFHS-5, which contain no personally identifiable information. Access to the dataset was granted by the Demographic and Health Surveys (DHS) Program following submission and approval of a data request form, in line with the DHS data use policy ( https://www.dhsprogram.com/data/Using-Datasets-for-Analysis.cfm ). As the data are fully de-identified and accessed under a standard data use agreement, this study is exempt from additional institutional ethical review under guidelines governing secondary analysis of anonymized survey data. Results Table 1 summarizes the characteristics of 155,624 women included in the analysis. Most women were aged 20–29 years (73.9%) and had secondary education (53.4%). Nearly four-fifths (77.9%) reported at least one pregnancy-related problem, and 70.1% delivered a normal-sized child. About two-thirds of deliveries occurred in government health facilities (68.5%), while 31.5% were in private facilities. First-order births accounted for 36.4% of deliveries, and 40.0% were second or third births with an interval of more than 24 months. The sample was predominantly rural (70.4%) and Hindu (80.3%), with Other Backward Classes forming the largest social group (45.7%). Antenatal care utilization was relatively high, with 62.1% of women reporting four or more ANC visits. Table 1 Sample size of last birth by background characteristic in India, 2019-21 Background Characteristics Frequency Percentage Mother's age at child birth < 20 year 15888 10.2 20–29 year 114977 73.9 30 + year 24759 15.9 Mother's schooling No Education 25,836 16.6 Primary 17,394 11.2 Secondary 83,114 53.4 Higher 29,280 18.8 Pregnancy Problem No 32,869 22.1 Yes 1,15,584 77.9 Size of the child Bigger than normal 30,191 19.5 Normal 1,08,564 70.1 Less than normal 16,202 10.5 Registered with ANM 59,346 40.0 Not ANM 89,159 60.0 Place of delivery Government hospital 1,06,649 68.5 Private hospital 48,975 31.5 Birth order & Birth interval First birth order 56,597 36.4 2/3 BO & 24months 62,200 40.0 4 + BO & 24 months 12,443 8.0 Wealth index Poorest 30,623 19.7 Poorer 32,244 20.7 Middle 31,555 20.3 Richer 31,845 20.5 Richest 29,356 18.9 Residence Urban 46,149 29.7 Rural 1,09,475 70.4 Religion Hindu 1,24,924 80.3 Non-Hindu 30,700 19.7 Social Status SC/ST 49,059 33.2 OBC 67,555 45.7 Others 31,327 21.2 Geographic region North 17,739 11.4 Central 39,928 25.7 East 40,635 26.1 North-east 5,956 3.8 West 22,487 14.5 South 28,879 18.6 Number of ANC visits = 4 visits 95,398 62.1 Age at marriage =25 years 13,673 8.9 Ever Pregnancy Termination No 1,30,621 83.9 Yes 25,003 16.1 Total 1,55,624 100.0 Table 2 shows that the prevalence of the C-section delivery by background characteristics with urban and rural residence in India. Results showed that C-section delivery prevalence was higher in urban areas (36.6%) than in rural areas (22.3%). The prevalence of C-section deliveries increased with age, with rates of 28.3% in women under the age of 20, 35.3% in women between the ages of 20 and 29, and 44.2% in women over the age of 30. The prevalence of C-section delivery was 18.8% among uneducated women, 24% among those with primary education, 34.5% among those with secondary education, and 48.4% among those with higher education in urban areas, which was higher than in rural areas. The reason for 37.3% of C-section deliveries was complications with pregnancy. Furthermore, 37% of C-section births involved children who were larger or smaller than average in size. Results from the multilevel logistic regression analysis on c-section deliveries are shown in Table 2 Prevalence of caesarean delivery by background characteristics stratified by place of residence, India, 2019-21 Background Characteristics Rural Urban Total Prevalence (%) Chi 2 Prevalence (%) Chi 2 Mother's age at child birth < 20 year 20.19 < 0.001 28.3 < 0.001 21.9 20–29 year 22.35 35.3 26.1 30 + year 23.97 44.2 31.6 Mother's schooling No Education 10.62 < 0.001 18.4 < 0.001 11.8 Primary 14.79 24.0 16.8 Secondary 24.6 34.5 27.4 Higher 37.46 48.4 42.9 Pregnancy Problem No 20.9 < 0.001 34.9 0.003 25.0 Yes 23.29 37.3 27.5 Size of the child Bigger than normal 25.44 < 0.001 37.4 0.007 28.8 Normal 21.35 36.3 25.9 Less than normal 23.23 37.5 27.4 Registered with ANM 22.88 0.001 36.5 0.108 27.5 Not ANM 22.32 36.0 26.0 Place of delivery Government hospital 13.4 < 0.001 24.8 < 0.001 16.1 Private hospital 48.13 51.0 49.3 Birth order & Birth interval First birth order 28.68 < 0.001 43.2 < 0.001 33.4 2/3 BO & 24months 22.33 35.7 26.5 4 + BO & 24 months 7.65 13.7 8.9 Wealth index Poorest 10.96 < 0.001 15.4 < 0.001 11.2 Poorer 18.71 22.7 19.1 Middle 27.24 30.1 27.9 Richer 32.18 36.1 33.9 Richest 37.06 43.4 41.4 Religion Hindu 21.93 < 0.001 37.8 < 0.001 26.3 Non-Hindu 24.18 33.1 27.5 Social Status SC/ST 17.9 < 0.001 33.3 < 0.001 21.3 OCB 22.86 36.5 26.9 Others 29.05 39.7 33.5 Geographic region North 15.3 < 0.001 26.4 < 0.001 18.9 Central 14.0 29.7 17.6 East 22.4 38.1 25.7 North-east 19.6 39.4 23.3 West 22.1 34.1 27.3 South 43.0 47.9 45.0 Number of ANC visits 1–3 visits 16.2 < 0.001 29.8 = 4 visits 26.6 39.2 30.9 Age at marriage < 18 years 16.8 < 0.001 25.4 =25 years 41.4 53.1 47.3 Ever Pregnancy Termination No 21.7 < 0.001 35.8 < 0.001 25.8 Yes 25.9 40.5 30.6 Total 22.3 36.6 26.6 The results of the multilevel logistic regression analysis (Table 3 ) and ICC values (supplementary Table S1 ) show significant differences in the probability of c-section deliveries at different levels, including PSU, district, and state. Model 2 seems to be the most suitable among the tested models, according to its AIC value. The likelihood of having a C-section was related to several variables. Compared to women between the ages of 20 and 29, those under 20 had a lower likelihood of having a C-section, while those over 30 had a higher likelihood. Furthermore, C-sections were less common in rural areas compared to urban areas. The likelihood of having a C-section increased with education level and was positively correlated with it. In rural areas, 24% of women have a c-section if their child is larger than average, and 16% if their child is smaller than average. When comparing private hospitals to government hospitals, rural areas have 6.2 times increase, and the urban regions have 3.2 times increase. Birth order and birth interval were both negatively associated with C-sections. C-sections were less common in the second and higher birth orders and birth intervals compared to the first birth order and interval. The wealth index was related to C-section delivery in a positive way. When compared to the poorest wealth index, the poorest are 36% more likely, the middle 68% more likely, the rich 86% more likely, and the wealthiest 85% more likely to have a C-section. Religion and C-section rates were negatively correlated but positively correlated with social status. In comparison to SC/ST categories in rural areas, OBC categories are 10% and others 17% more likely to undergo a C-section. In rural areas, ANC visits > = 4 were 15% more likely to result in a C-section than visits = 3. Age at marriage was positively correlated with C-sections. Compared to women under 18, those between the ages of 18 and 24 in rural areas have a 13% higher likelihood of having a c-section and those between the ages of 25 and over have a 44% higher likelihood. Of all women who have had pregnancies ended, 25% opt for C-sections. Table 3 Multilevel analysis for the last birth with caesarean delivery by background characteristics, 2019-21 Background Characteristics Urban Rural Total Model 1 (AOR) Model 2 (AOR) Model 1 (AOR) Model 2 (AOR) Model 1 (AOR) Model 2 (AOR) Mother’s age at child birth Null Model Full Model Null Model Full Model Null Model Full Model 20–29 year < 20 year 0.76*** [0.67,0.87] 0.74*** [0.69,0.80] 0.75*** [0.70,0.80] 30 + year 1.39*** [1.29,1.51] 1.36*** [1.28,1.44] 1.37*** [1.31,1.44] Place of residence Urban Rural 0.90*** [0.86,0.94] Mother’s schooling No Education Primary 1.16 [0.99,1.35] 1.12** [1.04,1.21] 1.14*** [1.06,1.22] Secondary 1.37*** [1.21,1.55] 1.29*** [1.21,1.38] 1.32*** [1.25,1.39] Higher 1.49*** [1.30,1.70] 1.44*** [1.33,1.56] 1.43*** [1.33,1.52] Pregnancy Problem No Yes 1.04 [0.97,1.12] 1.09*** [1.04,1.14] 1.07*** [1.03,1.11] Size of the child Normal Bigger than normal 1.11** [1.03,1.19] 1.24*** [1.18,1.29] 1.20*** [1.16,1.25] Less than normal 1.23*** [1.12,1.35] 1.16*** [1.09,1.23] 1.18*** [1.12,1.24] Registered with ANM Not ANM 1.01 [0.95,1.07] 0.94** [0.90,0.98] 0.96* [0.93,0.99] Place of delivery Government hospital Private hospital 3.21*** [3.02,3.42] 6.20*** [5.93,6.48] 5.02*** [4.84,5.20] Birth order & Birth interval First birth order 2/3 BO & 24months 0.76*** [0.71,0.81] 0.77*** [0.73,0.80] 0.77*** [0.74,0.79] 4 + BO & 24 months 0.38*** [0.32,0.45] 0.39*** [0.35,0.43] 0.39*** [0.36,0.42] Wealth index Poorest Poorer 1.24 [0.99,1.54] 1.33*** [1.25,1.41] 1.36*** [1.28,1.44] Middle 1.54*** [1.25,1.90] 1.61*** [1.51,1.72] 1.68*** [1.59,1.79] Richer 1.85*** [1.50,2.27] 1.70*** [1.59,1.83] 1.86*** [1.75,1.98] Richest 1.83*** [1.48,2.27] 1.74*** [1.60,1.90] 1.85*** [1.72,1.99] Religion Hindu Non-Hindu 0.88*** [0.82,0.95] 0.89*** [0.84,0.95] 0.90*** [0.85,0.94] Social Status SC/ST OCB 0.98 [0.91,1.06] 1.10*** [1.05,1.15] 1.06** [1.02,1.10] Others 1.08 [1.00,1.18] 1.22*** [1.15,1.29] 1.17*** [1.12,1.23] Geographic region North Central 0.79 [0.39,1.62] 0.52 [0.24,1.14] 0.62 [0.29,1.29] East 1.14 [0.61,2.13] 1.09 [0.56,2.12] 1.15 [0.61,2.18] North-east 1.02 [0.58,1.79] 0.87 [0.47,1.59] 0.96 [0.55,1.69] West 0.88 [0.47,1.67] 0.56 [0.28,1.12] 0.70 [0.37,1.35] South 1.73 [0.97,3.06] 1.92* [1.04,3.56] 1.76 [0.99,3.15] Number of ANC visits = 4 visits 1.08* [1.01,1.15] 1.15*** [1.10,1.19] 1.12*** [1.08,1.16] Age at marriage =25 years 1.37*** [1.22,1.53] 1.44*** [1.33,1.56] 1.41*** [1.32,1.51] Ever Pregnancy Termination No Yes 1.25*** [1.17,1.35] 1.20*** [1.14,1.26] 1.22*** [1.17,1.27] State, District and PSU level Variations Variance for State 0.52 [0.31 0.87] 0.25 [0.15 0.42] 1.03 [0.62 1.69] 0.3 [0.18 0.49] 0.89 [0.55 1.44] 0.27 [0.17 0.45] Variance for Districts 0.19 [0.15 0.25] 0.11 [0.08 0.15] 0.35 [0.3 0.41] 0.14 [0.11 0.16] 0.35 [0.31 0.4] 0.12 [0.11 0.14] Variance for PSU 0.38 [0.31 0.46] 0.18 [0.14 0.25] 0.44 [0.39 0.49] 0.25 [0.21 0.29] 0.54 [0.49 0.59] 0.24 [0.21 0.28] Log-Likelihood -22502.98 -17113.126 -58070.242 -41832.031 -81202.157 -59091.079 AIC 45015.97 34294.25 116150.5 83732.06 162414.3 118252.2 BIC 45058.69 34577.46 116199.7 84056.31 162464.7 118595.1 The Fairlie decomposition shows that 93.76% of the urban–rural difference in caesarean section delivery is explained by observed characteristics, while 6.24% remains unexplained (Table 4 ). The explained component is driven primarily by maternal characteristics, with place of delivery emerging as the largest contributor, accounting for 49.86% of the explained gap. Other maternal factors such as age at marriage (6.09%), mother’s schooling (5.50%), and mother’s age at childbirth (4.60%), make moderate contributions to the explained difference. Household and community characteristics also play a substantial role, particularly wealth index (15.74%) and geographic region (8.25%), indicating that socioeconomic position and regional context significantly shape urban–rural disparities in caesarean delivery. In contrast, some factors such as ANC visits, pregnancy complications, and ANM registration explain only small portions of the disparity. Table 4 Fairlie decomposition analysis of the rural–urban disparity in caesarean section deliveries by child, maternal, and household characteristics Background Characteristics Coefficient S.E. % contribution explained Child characteristics Size of the child 0.00015 0.00005 -0.12 Birth order & Birth interval -0.00309 0.00030 2.47 Mother characteristics Mother’s schooling -0.00688 0.00148 5.50 Age at marriage -0.00762 0.00064 6.09 Mother’s age at child birth -0.00575 0.00063 4.60 Pregnancy Problem -0.00012 0.00002 0.10 Number of ANC visits -0.00335 0.00028 2.68 Registered with ANM -0.00005 0.00017 0.04 Place of delivery -0.06234 0.00075 49.86 Ever Pregnancy Termination -0.00039 0.00006 0.32 Household and Community characteristics Wealth index -0.01968 0.00376 15.74 Religion -0.00046 0.00018 0.37 Social Status -0.00513 0.00051 4.10 Geographic region -0.01031 0.00185 8.25 Explained 93.76 Unexplained 6.24 Discussion This study, utilizing nationally representative data from NFHS-5 (2019–21), reveals significant urban–rural disparities in caesarean section (CS) delivery in India, with an overall prevalence of 26.6%. While CS rates in urban areas (36.6%) substantially exceed WHO-recommended thresholds (10–15%), rural areas (22.3%) reflect a more moderate but rising trend. Our findings reinforce the persistent inequality in obstetric care access and utilization across India, underscoring the dual challenges of CS overuse in urban private facilities and potential underuse in rural public settings, a pattern consistent with the “too much, too soon” versus “too little, too late” framework in maternal healthcare. The third Sustainable Development Goal (SDG 3) seeks to ensure healthy lives and promote well-being for all people of all ages, including maternal health. However, safe motherhood and maternal mortality reduction are critical components of SDG 3 (Hak et al., 2016). Consistent with prior evidence, CS delivery was more common among women who were older, more educated, and from wealthier households (Ghos S, 2010; Kambo et al., 2002 ; Mishra et al., 2002; Padmadas et al., 2000 ). Advanced maternal age was associated with a higher likelihood of CS, possibly reflecting increased obstetric risk and provider caution (Richard et al., 2016; Rydahl et al., 2019 ). Similarly, higher education and wealth may influence delivery mode through greater healthcare access, preferences for medical interventions, and higher utilization of private facilities (Betran et al., 2016; Chu et al., 2012 ; Gebremedhin, 2014 ). These patterns underscore the role of socioeconomic gradients in shaping obstetric decision-making and highlight the need for equity-focused maternal health strategies. The study also found that CS likelihood varied by reproductive factors, religion, and antenatal care utilization. Women receiving more ANC visits had a higher probability of CS, a finding that may reflect closer medical surveillance, differential provider practices, or variations in care quality across settings (Divyamol et al., 2016 ; Yaya et al., 2018 ). Additionally, lower CS rates among certain religious groups suggest the influence of cultural norms and beliefs, warranting further qualitative investigation (Ghos S, 2010). Taken together, these findings emphasize the importance of strengthening rural public obstetric services while regulating urban private healthcare, promoting evidence-based guidelines, and ensuring that CS is used appropriately to safeguard maternal and neonatal health. The urban-rural disparity in Cesarean Section (CS) rates is a significant public health concern, with Fairlie decomposition analysis revealing that 93.8% of this gap is attributable to observable characteristics, primarily the place of delivery, which accounts for 49.9% of the explained disparity (Abate et al., 2025 ). This substantial contribution from the place of delivery is consistent across various studies examining socioeconomic inequalities in CS utilization (Samuel et al., 2021 ; Panda et al., 2020 ). Deliveries in private hospitals are strongly associated with markedly higher odds of CS, with an Adjusted Odds Ratio (AOR) of 5.02. Decomposition analysis also shows that with place of delivery emerging as the largest contributor, accounting for 49.86% of the explained gap. This finding is corroborated by research from various regions, including India, where private facilities contribute significantly to the rising CS rate (Panda et al., 2020 ; Sk, R. 2020 ; Bhatia 2020). For instance, a study in West Bengal, India, highlighted that women delivering in private facilities had higher odds of undergoing a CS compared to those in public facilities (Sarkar S. 2020 ). Similarly, in Nepal, a substantial rise in CS rates has been observed in private institutions (Bhandari et al., 2020 ). This pattern suggests that financial incentives, defensive medicine practices, and patient preferences for "controlled" deliveries, particularly among educated and affluent urban women, may contribute to an increase in medically unnecessary CS in private settings (Tuner et al., 2020; Panda et al., 2020 ; Kumar & Lakhtakia 2021 ). The presence of financial incentives has been shown to influence healthcare professionals' decisions, though the impact can vary. The global trend also indicates that CS rates in private hospitals often exceed those in public facilities, even after adjusting for clinical indications (Tuner et al., 2020; Ahmad et al., 2024 ). Conversely, rural women's greater reliance on public facilities, often compounded by infrastructural and human resource deficiencies, likely leads to the underutilization of medically indicated CS, thereby reflecting systemic inequities in access to emergency obstetric care (Abate et al., 2025 ; Abdulla et al., 2023 ; Bobo et al., 2021 ). Studies from Sub-Saharan Africa and Bangladesh demonstrate significant urban-rural disparities in the utilization of maternal health services, including access to health facilities for delivery and professional assistance, with rural areas often lagging behind (Samuel at al., 2021; Abdulla et al., 2023 ). Socioeconomic gradients further stratify CS access. Higher maternal education, greater wealth quintile, and higher social status (e.g., OBC/Others versus Scheduled Castes/Scheduled Tribes, or SC/ST) are consistently associated with an increased likelihood of CS. These disparities align with findings from various low- and middle-income countries, including those in Sub-Saharan Africa and South Asia (Abate et al., 2025 ; Shibre et al., 2020 ; Wyatt et al., 2021 ). Maternal factors significantly contribute to urban-rural disparities in Cesarean Section (CS) rates, with age at marriage (6.09%), mother's schooling (5.50%), and mother's age at childbirth (4.60%) identified as moderate contributors to the explained difference (Abate et al., 2025 ). These findings align with broader observations that maternal demographic and educational attributes influence health-seeking behaviors and healthcare utilization (Zahroh et al., 2020 ; Abdulla et al., 2023 ). Furthermore, household and community characteristics play a substantial role, notably the wealth index (15.74%) and geographic region (8.25%) (Abate et al., 2025 ). This underscores that socioeconomic position and regional context are critical in shaping urban-rural CS disparities, a pattern consistently reported across low- and middle-income countries where wealth and residential location dictate access to quality maternal healthcare (Samuel at al., 2021; Yaya et al., 2020 ). Conversely, factors like (ANC) visits, pregnancy complications, and Auxiliary Nurse Midwife (ANM) registration explain only small portions of the overall urban-rural CS disparity. This suggests that while these clinical and care-seeking behaviours are important, their influence on the urban-rural CS gap is less pronounced compared to fundamental socioeconomic and demographic determinants. The limited contribution of ANC visits, for example, could indicate that while essential for maternal health, their impact on the mode of delivery within differing urban and rural infrastructures varies (Abate at al., 2025). Conclusion CS delivery in India is profoundly shaped by place of residence, healthcare sector, and socioeconomic advantage. The urban–private nexus drives CS overuse, while rural–public settings may inadequately provide life-saving surgical care. Achieving equitable, evidence-based maternity care requires targeted policy action that regulates private-sector excess, fortifies public-sector capacity, and prioritizes maternal well-being over commercial or convenience-driven practices. Such efforts are essential not only for improving birth outcomes but also for advancing progress toward Sustainable Development Goal 3 ensuring healthy lives and well-being for all. Limitations and Future Research This study has several limitations. The cross-sectional design precludes causal inference. Self-reported data may be subject to recall and social desirability biases. Lack of clinical indications for CS limits assessment of appropriateness. Furthermore, unobserved confounders such as provider characteristics, hospital policies, and maternal request could influence CS outcomes. Future research should incorporate mixed-methods approaches to explore provider motivations, patient preferences, and context-specific barriers. Longitudinal studies tracking CS trajectories and linked clinical-data analyses would enhance understanding of medical necessity versus discretionary practice. Policy Implications Our findings highlight an urgent need for dual-focused interventions: curbing non-medically indicated CS in urban-private facilities while ensuring timely access to emergency obstetric surgery in rural-public settings. Regulatory measures including CS audits, evidence-based clinical guidelines, and transparent billing in private hospitals could mitigate supplier-induced demand. Concurrently, strengthening rural emergency obstetric care through staff training, infrastructure upgradation, and demand-side financing (e.g., conditional cash transfers) may address underuse. Promoting midwifery-led care and vaginal birth after caesarean (VBAC) in both sectors could further rationalize CS rates. Additionally, community awareness programs addressing CS risks and benefits may empower informed decision-making, particularly among less-educated and rural women. Declarations Patient and Public Involvement Patients and the public were not involved in the design, conduct, reporting, or dissemination of this research. This study used secondary anonymised data from the National Family Health Survey (NFHS-5, 2019–21). Ethics approval and consent to participate : This study is based on secondary analysis of anonymised data from the National Family Health Survey (NFHS-5), which is publicly available upon reasonable request from the DHS Program. Ethical approval for the original survey was obtained by the implementing agencies, and no additional ethical approval was required for this secondary analysis. Competing Interest: The authors declare no competing interests. Consent for publication: Not applicable Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution PP, MD, MS conceptualised the study, and MS, PP, conducted all the data analyses, HK, MD, PP, MS analysed the results and drafted the manuscript. HK and MS had done the critical revision. All the authors read and approved the manuscript. Acknowledgement Not applicable Data Availability We used open-source secondary data, easily available on request [https://dhsprogram.com/data/available-datasets.cfm](https:/dhsprogram.com/data/available-datasets.cfm) . References Abate BJ, Gedefaw GD, Asmare TB, Demissie B, Gobezie NZ, Wubet HB, Adeleye K. (2025). Prevalence, urban-rural disparity and determinants of caesarean section delivery among women delivering at health facilities in 48 low-and middle-income countries: a multilevel and decomposition analysis. BMJ Global Health, 10(12). Abdulla F, Hossain MM, Rahman MM, Rahman MS, Rahman A. Risk factors of caesarean deliveries in urban–rural areas of Bangladesh. Front Reproductive Health. 2023;5:1101400. Ahmad F, Meyer E, Higgs M. Financial incentives and prosocial motivation among physicians in a faith-based society. Public Perform Manage Rev. 2024;47(5):1302–25. Ananna MN, Hossain MB. Caesarean Delivery-a Pressured Choice for Women: A Meta-Analysis of 23 Cross-Sectional Studies. Dhaka Univ J Sci. 2023;71(1):1–5. Arendt F, Markiewitz A, Mestas M, Scherr S. COVID-19 pandemic, government responses, and public mental health: Investigating consequences through crisis hotline calls in two countries. Soc Sci Med. 2020;265:113532. Betrán AP, Ye J, Moller AB, Zhang J, Gülmezoglu AM, Torloni MR. (2016). The increasing trend in caesarean section rates: global, regional and national estimates: 1990–2014. PLoS ONE, 11(2), e0148343. Bhandari AK, Dhungel B, Rahman M. Trends and correlates of cesarean section rates over two decades in Nepal. BMC Pregnancy Childbirth. 2020;20(1):763. Bhartia A, Sen Gupta Dhar R, Bhartia S. Reducing caesarean section rate in an urban hospital serving women attending privately in India–a quality improvement initiative. BMC Pregnancy Childbirth. 2020;20(1):556. Bhatia M, Banerjee K, Dixit P, Dwivedi LK. Assessment of variation in cesarean delivery rates between public and private health facilities in India from 2005 to 2016. JAMA Netw open. 2020;3(8):e2015022–2015022. Bhatnagar DM, Jharbade DH, Porwal DS. (2023). A RETROSPECTIVE STUDY OF PREVALENCE AND MAJOR INDICATIONS OF CESAREAN SECTION IN A TERTIARY CARE HOSPITAL IN INDIA. Blinder AS. (1973). Wage discrimination: reduced form and structural estimates. J Hum Resour, 436–55. Bobo FT, Asante A, Woldie M, Dawson A, Hayen A. (2021). Spatial patterns and inequalities in skilled birth attendance and caesarean delivery in sub-Saharan Africa. BMJ Global Health, 6(10), e007074. Cai WW, Marks JS, Chen CH, Zhuang YX, Morris L, Harris JR. Increased cesarean section rates and emerging patterns of health insurance in Shanghai, China. Am J Public Health. 1998;88(5):777–80. Chaurasia AR. (2022). Urban-Rural Disparity in Family Planning Use in India, 1992–2021. Available at SSRN 4110330. Chu K, Cortier H, Maldonado F, Mashant T, Ford N, Trelles M. Cesarean section rates and indications in sub-Saharan Africa. a multi-country study from Medecins sans Frontieres; 2012. Darnal N, Dangal G. Maternal and fetal outcome in emergency versus elective caesarean section. J Nepal Health Res Counc. 2020;18(02):186–9. Divyamol N, Raphael L, Koshy N. Caesarean section rate and its determinants in a rural area of South India. Int J Community Med Public Health. 2016;3(10):2836–40. Fairlie RW. An extension of the Blinder-Oaxaca decomposition technique to logit and probit models. J Econ Soc Meas. 2005;30(4):305–16. Garson GD. Hierarchical linear modeling: Guide and applications. Sage; 2013. Gebremedhin S. Trend and socio-demographic differentials of Caesarean section rate in Addis Ababa, Ethiopia: analysis based on Ethiopia demographic and health surveys data. Reproductive health. 2014;11(1):14. Goldstein H. Multilevel statistical models. Wiley; 2011. Gondwe T, Betha K, Kusneniwar GN, Bunker CH, Tang G, Simhan H, Haggerty CL. Adverse infant outcomes associated with caesarean section delivery in India. Int Health. 2020;12(5):411–6. Govil D, Mohanty SK, Narzary PK. Catastrophic household expenditure on caesarean deliveries in India. J Popul Res. 2020;37(2):139–59. Gregory KD, Jackson S, Korst L, Fridman M. Cesarean versus vaginal delivery: whose risks? Whose benefits? Am J Perinatol. 2012;29(01):07–18. Hák T, Janoušková S, Moldan B. Sustainable Development Goals: A need for relevant indicators. Ecol Ind. 2016;60:565–73. Hlavac M. (2014). oaxaca: Blinder-Oaxaca decomposition in R. Available at SSRN 2528391. International Institute for Population Sciences (IIPS), & ICF. National Family Health Survey (NFHS-5), 2019–21: India. Mumbai, India: IIPS; 2021. International Institute for Population Sciences (IIPS), & Macro International. National Family Health Survey (NFHS-3), 2005–06: India. Volume 1. Mumbai, India: IIPS; 2007. Kambo I, Bedi N, Dhillon BS, Saxena NC. A critical appraisal of cesarean section rates at teaching hospitals in India. Int J Gynecol Obstet. 2002;79(2):151–8. Kumar R, Lakhtakia S. Rising cesarean deliveries in India: medical compulsions or convenience of the affluent? Health Care Women Int. 2021;42(4–6):611–35. Lumbiganon P, Laopaiboon M, Gülmezoglu AM, Souza JP, Taneepanichskul S, Ruyan P, Villar J. Method of delivery and pregnancy outcomes in Asia: the WHO global survey on maternal and perinatal health 2007–08. Lancet. 2010;375(9713):490–9. Mishra US, Ramanathan M. Delivery-related complications and determinants of caesarean section rates in India. Health Policy Plann. 2002;17(1):90–8. Mose A, Abebe H. Magnitude and associated factors of caesarean section deliveries among women who gave birth in Southwest Ethiopia: institutional-based cross-sectional study. Archives Public Health. 2021;79(1):158. O’brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673–90. Oaxaca R. (1973). Male-female wage differentials in urban labor markets. Int Econ Rev, 693–709. Oinam J, Kongjenbam S, Singh YN. (2020). Prevalence of caesarean section and womens' attitude towards caesarean section in Manipur, North-Eastern India. Padmadas SS, Nair SB, KR AK. Caesarean section delivery in Kerala, India: evidence from a national family health survey. Soc Sci Med. 2000;51(4):511–21. Panda BK, Nayak I, Mishra US. Determinant of inequality in cesarean delivery in India: a decomposition analysis. Health Care Women Int. 2020;41(7):817–32. Potter JE, Berquó E, Perpétuo IH, Leal OF, Hopkins K, Souza MR, de Carvalho Formiga MC. Unwanted caesarean sections among public and private patients in Brazil: prospective study. BMJ. 2001;323(7322):1155–8. Powers DA, Yoshioka H, Yun MS. mvdcmp: Multivariate decomposition for nonlinear response models. Stata J. 2011;11(4):556–76. Rai SD, Poobalan A, Jan R, Bogren M, Wood J, Dangal G, Shahid F. Caesarean Section rates in South Asian cities: Can midwifery help stem the rise? Journal of Asian Midwives (JAM); 2019. Richards MK, Flanagan MR, Littman AJ, Burke AK, Callegari LS. Primary cesarean section and adverse delivery outcomes among women of very advanced maternal age. J Perinatol. 2016;36(4):272–7. Rydahl E, Declercq E, Juhl M, Maimburg RD. (2019). Cesarean section on a rise—Does advanced maternal age explain the increase? A population register-based study. PLoS ONE, 14(1), e0210655. Sahoo H, Jeermison RK. Repeated caesarean section delivery in India. Child Youth Serv Rev. 2020;116:105258. Samuel O, Zewotir T, North D. Decomposing the urban–rural inequalities in the utilisation of maternal health care services: evidence from 27 selected countries in Sub-Saharan Africa. Reproductive Health. 2021;18(1):216. Sarkar S. Prevalence and determinants of the use of caesarean section (CS) in the dichotomy of ‘public’and ‘private’health facilities in West Bengal. India. Clin Epidemiol Global Health. 2020;8(4):1377–83. Sarkar S. Prevalence and determinants of the use of caesarean section (CS) in the dichotomy of ‘public’and ‘private’health facilities in West Bengal. India. Clin Epidemiol Global Health. 2020;8(4):1377–83. Sharma S, Jaiswal AK, Singh RK, Kumar P, Mehra S. Differential access to facilities for medical termination of pregnancy and delivery in India: a secondary analysis. Clin Epidemiol Global Health. 2021;12:100825. Shibre G, Zegeye B, Ahinkorah BO, Keetile M, Yaya S. Magnitude and trends in socio-economic and geographic inequality in access to birth by cesarean section in Tanzania: evidence from five rounds of Tanzania demographic and health surveys (1996–2015). Archives Public Health. 2020;78(1):80. Shukla M, Mohan M, van Duinen A, Gadgil A, Bakker J, Bhushan P, Roy N. (2022). Assessing geographical and economic inequalities in caesarean section rates between the districts of Bihar, India: a secondary analysis of the National Family Health Survey. BMJ open, 12(1), e055326. Simmons E, Lane K, Rao SR, Kurhe K, Patel A, Hibberd PL. (2021). Trends in cesarean section rates in private and public facilities in rural eastern Maharashtra, India from 2010–2017. PLoS ONE, 16(8), e0256096. Singh SK, Aditi, Sharma SK. Changing the discourse on caesarean births in India: issues emerging from NFHS-5 (2019–2021). SN Social Sci. 2022;2(7):103. Singh SK, Vishwakarma D, Sharma SK. Prevalence and determinants of voluntary caesarean deliveries and socioeconomic inequalities in India: Evidence from National Family Health Survey (2015-16). Clin Epidemiol Global Health. 2020;8(2):335–42. Sk R. (2020). Does delivery in private hospitals contribute largely to Caesarean Section births? A path analysis using generalised structural equation modelling. PLoS ONE, 15(10), e0239649. Sufang G, Padmadas SS, Fengmin Z, Brown JJ, Stones RW. Delivery settings and caesarean section rates in China. Bull World Health Organ. 2007;85:755–62. Wyatt S, Silitonga PII, Febriani E, Long Q. Socioeconomic, geographic and health system factors associated with rising C-section rate in Indonesia: a cross-sectional study using the Indonesian demographic and health surveys from 1998 to 2017. BMJ open. 2021;11(5):e045592. Yaya S, Bishwajit G, Shah V. (2016). Wealth, education and urban–rural inequality and maternal healthcare service usage in Malawi. BMJ global health, 1(2), e000085. Yaya S, Uthman OA, Amouzou A, Bishwajit G. Disparities in caesarean section prevalence and determinants across sub-Saharan Africa countries. Global health Res policy. 2018;3(1):19. Yaya S, Zegeye B, Idriss-Wheeler D, Shibre G. Inequalities in caesarean section in Burundi: evidence from the Burundi demographic and health surveys (2010–2016). BMC Health Serv Res. 2020;20(1):652. Zahroh RI, Disney G, Betrán AP, Bohren MA. (2020). Trends and sociodemographic inequalities in the use of caesarean section in Indonesia, 1987–2017. BMJ global health, 5(12). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial26032026.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9229990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621062621,"identity":"1c2fe0b7-0a01-4c2e-b23b-9d9bc5a08065","order_by":0,"name":"Priyanka Patel","email":"","orcid":"","institution":"Newcomb Institute Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Patel","suffix":""},{"id":621062622,"identity":"806515f8-6014-4137-9792-ea42307235ce","order_by":1,"name":"Milan Das","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Milan","middleName":"","lastName":"Das","suffix":""},{"id":621062623,"identity":"9e3a2e22-34cf-4fb5-a7cd-82980867d4b0","order_by":2,"name":"Harpreet Kour","email":"","orcid":"","institution":"Jawaharlal Nehru Medical College, KLE Academy of Higher Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Harpreet","middleName":"","lastName":"Kour","suffix":""},{"id":621062624,"identity":"5fe0850a-4acb-4b9d-8819-93bf0b78bb57","order_by":3,"name":"Mayank Singh","email":"data:image/png;base64,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","orcid":"","institution":"KLE Academy of Higher Education and Research","correspondingAuthor":true,"prefix":"","firstName":"Mayank","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2026-03-26 06:26:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9229990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9229990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106908660,"identity":"d6eeb527-3197-4bdf-9aa9-6ea4078b8f8e","added_by":"auto","created_at":"2026-04-14 16:05:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of sample selection and classification of caesarean section delivery by place of residence\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9229990/v1/9611848fe30e6cf69d9f7f5d.png"},{"id":106960981,"identity":"9088d6aa-6a62-42a7-ac1f-a1a8ea61741d","added_by":"auto","created_at":"2026-04-15 09:23:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2334582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9229990/v1/65bba89d-2a45-4079-bc28-cb08b091574c.pdf"},{"id":106908661,"identity":"ae12b028-f73d-47e9-940c-3fb7386132ff","added_by":"auto","created_at":"2026-04-14 16:05:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14778,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial26032026.docx","url":"https://assets-eu.researchsquare.com/files/rs-9229990/v1/82f2d91fbca3bdd47e141d4c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying Urban–Rural Disparities in Caesarean Section Deliveries in India: A Multilevel and Fairlie Decomposition Approach","fulltext":[{"header":"Key Message","content":"\u003cp\u003e\u003cstrong\u003eWhat is already known on this topic\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"5\"\u003e\n \u003cli\u003eCaesarean section rates have increased in India, with higher prevalence in urban areas and private healthcare facilities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eSocioeconomic and healthcare factors contribute to disparities in caesarean delivery.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"5\"\u003e\n \u003cli\u003eCaesarean delivery was substantially higher in urban areas and private hospitals, with private-sector delivery increasing the odds fivefold.\u003c/li\u003e\n \u003cli\u003eFairlie decomposition showed that 93.8% of the rural\u0026ndash;urban gap was explained, with place of delivery contributing nearly half of the disparity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice or policy\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"5\"\u003e\n \u003cli\u003eFindings highlight the need to regulate unnecessary caesarean deliveries in private facilities and improve equitable access to obstetric care.\u003c/li\u003e\n \u003cli\u003eAddressing structural inequalities may help ensure appropriate and evidence-based use of caesarean delivery.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eA Caesarean section is a surgical procedure in which the baby is delivered through incisions in the abdominal and uterine walls and is generally recommended when vaginal delivery poses risks to the mother or the fetus (Betr\u0026aacute;n et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gregory et a., 2012). Over time, the use of caesarean delivery has shown a marked upward trend across many countries, reflecting changing obstetric practices and patterns of childbirth. For instance, India experiencing a substantial rise from 3% in 1992\u0026ndash;93 to 11% in 2005\u0026ndash;06, and further to 22% in 2019\u0026ndash;21, with nearly 30% of all births in India (International Institute for Population Sciences (IIPS) \u0026amp;ICF, 2021; Singh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While Caesarean delivery is a crucial life-saving intervention, its rising utilization beyond medically recommended levels raised significant public health concerns. Unnecessary caesarean sections are associated with increased maternal and neonatal morbidity and mortality, higher healthcare costs, and avoidable medical risks, underscoring the need to understand the factors driving this upward trend (Lumbiganon et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bhatnagar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Govil et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) recommends that CS rates should ideally be between 10% and 15% of all deliveries (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rai et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gondwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although essential in specific obstetric situations but rates exceeding this threshold often suggest non-medically indicated procedures, leading to short- and long-term complications for both mothers and infants (Gondwe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Darnal \u0026amp; Dangal, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Short-term risks for mothers include hemorrhage, infection, anesthesia-related complications, uterine rupture, renal failure, obstetric shock, cardiac arrest, venous thromboembolism, and severe puerperal infection (Arendt et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Long-term consequences can involve increased likelihood of bowel obstruction due to pelvic adhesions, subfertility, decreased satisfaction with the birthing experience, shorter breastfeeding duration, and less satisfying mother-child interactions (Ananna \u0026amp; Hossain; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For infants, CS delivery influences initial colonization of the gut microbiome, potentially affecting long-term immune system development. Moreover, the increased risk of uterine rupture in subsequent pregnancies after a CS can lead to preterm birth, stillbirth, and maternal death (Sahoo \u0026amp; Jeermison; \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In low-resource settings, these risks are further amplified due to limited access to quality surgical care and post-operative management. Despite such concerns, caesarean section rates have reached 30\u0026ndash;50% in several high- and middle-income countries including Germany, Italy, France, the United States, and Australia (Sufang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In several middle-income countries, including Brazil, China, and India, the private healthcare sector plays a substantial role in maternity care, particularly in urban areas, and is often associated with markedly higher caesarean section rates compared with public facilities (Sufang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Potter et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant urban\u0026ndash;rural disparity in Caesarean section (CS) rates persists in India, with NFHS-5 (2019\u0026ndash;21) reporting a national prevalence of nearly 30%, largely driven by substantially higher rates in urban areas, particularly in private facilities where CS prevalence frequently exceeds 40\u0026ndash;50%, compared with 15\u0026ndash;20% in rural settings (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kumar \u0026amp; Lakhtakia, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oinam et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chaurasia, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This differential reflects not only variations in obstetric risk but also structural and systemic factors, most notably the greater reliance on private healthcare in urban areas, which consistently report significantly higher CS rates than public facilities (Bhatia et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Sarkar, S. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Simmons et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Evidence from rural eastern Maharashtra (2010\u0026ndash;2017) demonstrates rapidly increasing CS rates concentrated in private institutions, and multiple studies identify delivery in private hospitals as a key determinant of CS use (Sk, R. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Simmons et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These patterns are commonly attributed to financial incentives, defensive medical practices, provider convenience, and maternal preferences, including elective CS without medical indication, which is more prevalent among educated and affluent women in private-sector settings (Bhartia et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, rural areas often experience constrained access to skilled birth attendants and comprehensive emergency obstetric care, leading to lower CS rates even when clinically indicated a \u0026ldquo;too little, too late\u0026rdquo; scenario while urban private facilities exemplify a \u0026ldquo;too much, too soon\u0026rdquo; pattern (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Persistent gaps in institutional delivery coverage and unequal availability of emergency obstetric services, including CS capability and blood transfusion, further reinforce this urban\u0026ndash;rural divide (Kumar \u0026amp; Lakhtakia, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mose \u0026amp; Abebe, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCaesarean section (CS) delivery in India reflects a pronounced urban\u0026ndash;rural divide, characterized by limited access to medically necessary CS in rural areas and excessive, often non-indicated use in urban private facilities, raising concerns about equity, quality of care, and health system efficiency. Rural women frequently face barriers such as inadequate obstetric infrastructure and delayed emergency care, while urban private-sector dominance, weak regulation, and financial incentives contribute to CS overuse. Although regulatory approaches such as clinical audits, fee transparency, evidence-based guidelines, and quality improvement initiatives have shown promise in rationalizing CS use, population-level evidence identifying the drivers of urban\u0026ndash;rural disparities remain limited. Therefore, this study is essential to systematically examine urban\u0026ndash;rural differentials in CS delivery in India along with the factors contributing this differential and to generate evidence that can inform targeted policies aimed at ensuring equitable access to life-saving obstetric interventions while curbing unnecessary surgical deliveries.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003eThe study used data from the National Family Health Survey (NFHS-5) 2019-21, a comprehensive survey conducted in several rounds and includes households all over India. The Ministry of Health and Family Welfare (MoHFW), Government of India, conducts this periodic survey, with the International Institute of Population Sciences (IIPS), Mumbai serving as the survey\u0026apos;s main agency. NFHS is a reputable source of information on various socioeconomic, demographic, and health indicators at the national and sub-national levels and is frequently referred to as India\u0026apos;s DHS. Such indicators include family planning, maternal and child health service utilization, fertility, mortality, and other pertinent variables. This cross-sectional survey collects the data from all states and union territories of India using standardized questionnaire administered in 18 Indian languages via computer assisted personnel interview (CAPI), employing a two-stage stratified sampling design in rural and urban areas. In the first stage, primary sampling units (villages in rural areas and census enumeration blocks in urban areas) were selected using probability proportional to size, followed by systematic selection of households in the second stage. The child recode file, which forms the basis of this analysis, contains detailed information on all live births along with mode of delivery occurring in the five years preceding each survey, linked to maternal and household characteristics from the corresponding women\u0026rsquo;s and household files. NFHS-5 includes data on 232,920 children born to women aged 15\u0026ndash;49. Data quality was rigorously maintained through uniform training protocols, field supervision, back-checks, and the use of standardized field-check tables (International Institute for Population Sciences (IIPS) \u0026amp;ICF, 2021).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSample Selection Procedure:\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sample selection process and distribution of caesarean section deliveries by place of residence. Out of 232,920 births reported in the last five years, 56,077 births were excluded as the analysis was restricted to the most recent birth, resulting in a final sample of 176,843 last births. Among these, 155,624 births occurred in health institutions and were included in the analysis. Of the institutional births, 29.65% were from urban areas (N\u0026thinsp;=\u0026thinsp;35,904) and 70.35% from rural areas (N\u0026thinsp;=\u0026thinsp;119,720). Caesarean section deliveries were substantially higher in urban areas, with 36.61% of institutional births delivered by caesarean section, compared to 22.33% in rural areas, highlighting a pronounced urban\u0026ndash;rural disparity in caesarean section utilization.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eVariable Description:\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003eOutcome variable\u003c/strong\u003e:\u003c/h2\u003e\n \u003cp\u003eThe current study focus on the last birth caesarean section and uses a binary outcome variable to assess the mode of delivery for the most recent birth among currently married women aged 15\u0026ndash;49 years by the place of residence. The variable specifically represented whether the delivery was via caesarean section and was based on the mother\u0026apos;s self-reported data. Given the importance of caesarean deliveries as an indicator of maternal health and healthcare access, their inclusion as the primary outcome variable is noteworthy. The binary nature of the outcome variable, measured using \u0026quot;yes\u0026quot; or \u0026quot;no\u0026quot; responses, made data analysis using appropriate statistical methods easier.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eExplanatory variables:\u003c/h3\u003e\n\u003cp\u003eThis study utilized several socio-demographic characteristics as explanatory variables. These included age at marriage, categorized as less than 18 years, 18\u0026ndash;24 years, and 25 years or older. Mother\u0026apos;s age at childbirth is less than 20 years, 20\u0026ndash;29 years, and 30 years or older. Mother\u0026apos;s schooling as no education, primary education, secondary education, and higher education. Other variables included pregnancy problems (yes or no), the size of the child (bigger than normal, normal, or less than normal), registration with ANM (yes or no), place of delivery (government hospital or private hospital), birth order and interval (first birth order, second or third birth order with intervals of less than 24 months, second or third birth order with intervals of more than 24 months, fourth or higher birth order with intervals of less than 24 months, and fourth or higher birth order with intervals of more than 24 months), ANC visits (less than or equal to three visits or more than three visits), ever pregnancy termination (no or yes), wealth index (poorest, poorer, middle, richer, and richest), place of residence (rural or urban), religion (Hindu or non-Hindu), social status (SC/ST, OBC, or others), and geographical region (North, Central, East, Northeast, West, or South).\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe study began with a descriptive analysis that determined the responders\u0026apos; frequency distribution and the prevalence of C-section deliveries. The next step was to conduct a bivariate analysis using a chi-square test to find any potential explanatory variables related to C-section delivery. A \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 or less was regarded as statistically significant. The variance inflation factor (VIF) was used to conduct a multicollinearity test on all significant explanatory factors found in the bivariate analysis to prevent multicollinearity among the explanatory variables. No evidence of collinearity between the explanatory variables was found, according to the results of the VIF test. O\u0026apos;Brien deemed the VIF values to be acceptable when they were less than 10 (O\u0026rsquo;brien, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Specification for Multilevel Logistic Regression\u003c/h2\u003e\n \u003cp\u003eA multilevel logistic regression model was used to analyse the variation in the prevalence of C-section delivery across different geographic levels. The data used in the analysis had a hierarchical structure consisting of four levels: with individuals at level 1, primary sampling unis (PSUs) at level 2, districts at level 3, and states/union territories at level 4.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 486px;\"\u003e\u003c/p\u003e\n \u003cp\u003eThe probability of caesarean delivery was modelled using a four-level random-intercept logistic regression model specified as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:Logit\\:[P\\left({Y}_{ijkl}=1\\right)=\\:{\\beta\\:}_{0}+\\:\\:\\sum\\:_{p=1}^{P}{\\beta\\:}_{p}{{X}_{pi}}_{jkl}+(\\:{u}_{0l}+\\:{u}_{0kl}+\\:{u}_{0jkl}\\:)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003eis the overall fixed intercept, representing the log-odds of C-section delivery for women in the reference category of all explanatory variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{pijkl}\\)\u003c/span\u003e\u003c/span\u003eis a vector of individual-level socioeconomic and demographic covariates (e.g., maternal age, parity, education, wealth status, antenatal care utilization, place of residence), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{p}\\)\u003c/span\u003e\u003c/span\u003eare the corresponding fixed-effect coefficients, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{0jkl}\\sim\\:N(0,{\\sigma\\:}_{u}^{2})\\)\u003c/span\u003e\u003c/span\u003erepresents the random effect at the PSU level, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{v}_{0kl}\\sim\\:N(0,{\\sigma\\:}_{v}^{2})\\)\u003c/span\u003e\u003c/span\u003erepresents the random effect at the district level, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{0l}\\sim\\:N(0,{\\sigma\\:}_{f}^{2})\\)\u003c/span\u003e\u003c/span\u003erepresents the random effect at the state/union territory level. These random intercepts capture unobserved contextual heterogeneity operating at different geographic levels, such as regional health system capacity, provider practices, and policy environments.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eTwo nested multilevel logistic regression models were fitted\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eModel I (Null Model)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA model without explanatory variables, including only random intercepts, was estimated to examine the extent of between-cluster variation in C-section delivery and to compute the Intra-Class Correlation Coefficient (ICC).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel II (Adjusted Model)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis model included selected socioeconomic and demographic characteristics of the respondents to assess their association with C-section delivery while accounting for clustering at PSU, district, and state levels.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe ICC was calculated using the latent variable method for logistic models as:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\text{ICC}=\\frac{{\\sigma\\:}_{u}^{2}+{\\sigma\\:}_{v}^{2}+{\\sigma\\:}_{f}^{2}}{{\\sigma\\:}_{u}^{2}+{\\sigma\\:}_{v}^{2}+{\\sigma\\:}_{f}^{2}+\\frac{{\\pi\\:}^{2}}{3}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\pi\\:}^{2}}{3}\\)\u003c/span\u003e\u003c/span\u003e represents the individual-level variance of logistic distribution. All models were estimated using maximum likelihood estimation. Results are presented as adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) (Goldstein, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Garson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFairlie Decomposition analysis\u003c/h3\u003e\n\u003cp\u003eTo quantify the extent to which differences in observed sociodemographic characteristics explain the rural\u0026ndash;urban disparity in caesarean section (C-section) deliveries, we employed the Fairlie Decomposition Analysis (FDA). FDA is a nonlinear decomposition technique suitable for binary outcomes estimated using logit or probit models (Fairlie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Powers et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This method extends the classical Blinder\u0026ndash;Oaxaca decomposition framework developed for linear regression models (Blinder, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Oaxaca, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) which has been widely criticized for its inappropriateness in nonlinear contexts such as logit and probit models due to issues related to model identification and scale dependence (Hlavac, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), to nonlinear probability models, thereby avoiding problems of identification and scale dependence inherent in applying linear decomposition to logit or probit models (Hlavac, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Specification for Fairlie Decomposition\u003c/h2\u003e\n \u003cp\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e denote the binary indicator of caesarean section delivery, coded as Y\u0026thinsp;=\u0026thinsp;1 if the woman delivered by caesarean section and Y\u0026thinsp;=\u0026thinsp;0 if the woman delivered by normal (vaginal) delivery. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{i}\\)\u003c/span\u003e\u003c/span\u003e denote place of residence, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S=1\\)\u003c/span\u003e\u003c/span\u003e indicates urban women and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S=0\\)\u003c/span\u003e\u003c/span\u003e indicates rural women. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e be a vector of explanatory variables capturing child, maternal, and household characteristics.\u003c/p\u003e\n \u003cp\u003ePredicted probabilities of caesarean section delivery were obtained from a multilevel logistic regression model (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Full Model for total column) estimated on the pooled sample:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{Y}}_{i}=F\\left({X}_{i}\\widehat{\\beta\\:}\\right)\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F(.)\\)\u003c/span\u003e\u003c/span\u003eis the multilevel logistic cumulative distribution function and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003eis the vector of estimated coefficients.\u003c/p\u003e\n \u003cp\u003eThe difference in mean predicted probabilities of caesarean section delivery between rural and urban women is given by:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}={\\stackrel{\\prime }{Y}}_{1}-{\\stackrel{\\prime }{Y}}_{0}=\\frac{1}{{N}_{1}}\\sum\\:_{i=1}^{{N}_{1}}F({X}_{1i}\\widehat{\\beta\\:})-\\frac{1}{{N}_{0}}\\sum\\:_{i=1}^{{N}_{0}}F({X}_{0i}\\widehat{\\beta\\:})\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003ewhere:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{\\prime }{Y}}_{1}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{\\prime }{Y}}_{0}\\)\u003c/span\u003e\u003c/span\u003e denote the average predicted probabilities of caesarean section delivery between rural and urban women, respectively;\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{1}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{0}\\)\u003c/span\u003e\u003c/span\u003e represent the sample sizes of the two groups;\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1i}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{0i}\\)\u003c/span\u003e\u003c/span\u003e are covariate vectors for individuals in the respective groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFollowing Fairlie (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the total difference in predicted probabilities is decomposed into explained and unexplained components as:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:{\\stackrel{\\prime }{Y}}_{1}-{\\stackrel{\\prime }{Y}}_{0}=\\left[\\frac{1}{{N}_{1}}\\sum\\:_{i=1}^{{N}_{1}}F({X}_{1i}{\\widehat{\\beta\\:}}_{1}\\right)-\\frac{1}{{N}_{0}}\\sum\\:_{i=1}^{{N}_{0}}F({X}_{0i}{\\widehat{\\beta\\:}}_{1}\\left)\\right]+\\left[\\frac{1}{{N}_{0}}\\sum\\:_{i=1}^{{N}_{0}}F({X}_{0i}{\\widehat{\\beta\\:}}_{1}\\right)-\\frac{1}{{N}_{0}}\\sum\\:_{i=1}^{{N}_{0}}F({X}_{0i}{\\widehat{\\beta\\:}}_{0}\\left)\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere the first term represents the portion of the caesarean section delivery gap attributable to differences in observed characteristics (explained component); and the second term captures differences due to coefficients and unobserved factors (unexplained component); and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}_{0}\\)\u003c/span\u003e\u003c/span\u003e denote coefficient vectors estimated separately for rural and urban women.\u003c/p\u003e\n \u003cp\u003eThe variable-specific contributions to the explained component were obtained using the Fairlie nonlinear decomposition procedure, which estimates the change in average predicted probabilities when the distribution of each explanatory variable is sequentially replaced between groups while holding other variables constant. This procedure is implemented within the decomposition algorithm and does not rely on linear approximations of mean covariate differences (Fairlie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Differences in sample sizes between groups were accounted for within the decomposition routine through matching based on predicted probabilities, as implemented in the statistical software. The explained component reflects the proportion of the caesarean section delivery gap attributable to differences in observable child, maternal, and household characteristics, including maternal age, education, pregnancy related problem, size of child, place of delivery, household wealth, residence, region, and exposure to mass media, while the unexplained component represents differences due to differential effects of these characteristics and unobserved factors.\u003c/p\u003e\n \u003cp\u003eStatistical analysis was conducted using STATA version 17.0, with all estimates derived using appropriate sampling weights to account for the complex survey design of the NFHS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEthics declaration\u003c/h2\u003e\n \u003cp\u003eEthical clearance for the National Family Health Survey-5 (NFHS-5) was obtained by the International Institute for Population Sciences (IIPS), Mumbai, from its Institutional Ethics Committee, and data collection was conducted in accordance with national and international ethical standards for survey research. The present study uses publicly available, anonymized secondary data from NFHS-5, which contain no personally identifiable information. Access to the dataset was granted by the Demographic and Health Surveys (DHS) Program following submission and approval of a data request form, in line with the DHS data use policy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dhsprogram.com/data/Using-Datasets-for-Analysis.cfm\u003c/span\u003e\u003c/span\u003e). As the data are fully de-identified and accessed under a standard data use agreement, this study is exempt from additional institutional ethical review under guidelines governing secondary analysis of anonymized survey data.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the characteristics of 155,624 women included in the analysis. Most women were aged 20\u0026ndash;29 years (73.9%) and had secondary education (53.4%). Nearly four-fifths (77.9%) reported at least one pregnancy-related problem, and 70.1% delivered a normal-sized child. About two-thirds of deliveries occurred in government health facilities (68.5%), while 31.5% were in private facilities. First-order births accounted for 36.4% of deliveries, and 40.0% were second or third births with an interval of more than 24 months. The sample was predominantly rural (70.4%) and Hindu (80.3%), with Other Backward Classes forming the largest social group (45.7%). Antenatal care utilization was relatively high, with 62.1% of women reporting four or more ANC visits.\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\u003eSample size of last birth by background characteristic in India, 2019-21\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's age at child birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;+\u0026thinsp;year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83,114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy Problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,15,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize of the child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBigger than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,08,564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegistered with\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot ANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,06,649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order \u0026amp; Birth interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst birth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2/3 BO \u0026amp; \u0026lt;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2/3 BO \u0026amp; \u0026gt; 24months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;BO \u0026amp; \u0026lt; 24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;BO \u0026amp; \u0026gt;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29,356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,09,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,24,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC/ST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographic region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28,879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of ANC visits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 3 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95,398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90,539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver Pregnancy Termination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,30,621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,55,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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 \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the prevalence of the C-section delivery by background characteristics with urban and rural residence in India. Results showed that C-section delivery prevalence was higher in urban areas (36.6%) than in rural areas (22.3%). The prevalence of C-section deliveries increased with age, with rates of 28.3% in women under the age of 20, 35.3% in women between the ages of 20 and 29, and 44.2% in women over the age of 30. The prevalence of C-section delivery was 18.8% among uneducated women, 24% among those with primary education, 34.5% among those with secondary education, and 48.4% among those with higher education in urban areas, which was higher than in rural areas. The reason for 37.3% of C-section deliveries was complications with pregnancy. Furthermore, 37% of C-section births involved children who were larger or smaller than average in size.\u003c/p\u003e \u003cp\u003eResults from the multilevel logistic regression analysis on c-section deliveries are shown in\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\u003ePrevalence of caesarean delivery by background characteristics stratified by place of residence, India, 2019-21\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's age at child birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;+\u0026thinsp;year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy Problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize of the child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBigger than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegistered with\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot ANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order \u0026amp; Birth interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst birth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2/3 BO \u0026amp; \u0026lt;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2/3 BO \u0026amp; \u0026gt; 24months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;BO \u0026amp; \u0026lt; 24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;BO \u0026amp; \u0026gt;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC/ST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographic region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of ANC visits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver Pregnancy Termination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e22.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e36.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e26.6\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\u003eThe results of the multilevel logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and ICC values (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) show significant differences in the probability of c-section deliveries at different levels, including PSU, district, and state. Model 2 seems to be the most suitable among the tested models, according to its AIC value. The likelihood of having a C-section was related to several variables. Compared to women between the ages of 20 and 29, those under 20 had a lower likelihood of having a C-section, while those over 30 had a higher likelihood. Furthermore, C-sections were less common in rural areas compared to urban areas. The likelihood of having a C-section increased with education level and was positively correlated with it. In rural areas, 24% of women have a c-section if their child is larger than average, and 16% if their child is smaller than average. When comparing private hospitals to government hospitals, rural areas have 6.2 times increase, and the urban regions have 3.2 times increase. Birth order and birth interval were both negatively associated with C-sections. C-sections were less common in the second and higher birth orders and birth intervals compared to the first birth order and interval. The wealth index was related to C-section delivery in a positive way. When compared to the poorest wealth index, the poorest are 36% more likely, the middle 68% more likely, the rich 86% more likely, and the wealthiest 85% more likely to have a C-section. Religion and C-section rates were negatively correlated but positively correlated with social status. In comparison to SC/ST categories in rural areas, OBC categories are 10% and others 17% more likely to undergo a C-section. In rural areas, ANC visits\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;4 were 15% more likely to result in a C-section than visits\u0026thinsp;=\u0026thinsp;3. Age at marriage was positively correlated with C-sections. Compared to women under 18, those between the ages of 18 and 24 in rural areas have a 13% higher likelihood of having a c-section and those between the ages of 25 and over have a 44% higher likelihood. Of all women who have had pregnancies ended, 25% opt for C-sections.\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 analysis for the last birth with caesarean delivery by background characteristics, 2019-21\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2 (AOR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s age at child birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNull Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFull Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNull Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFull Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNull Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eFull Model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76*** [0.67,0.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74*** [0.69,0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75*** [0.70,0.80]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;+\u0026thinsp;year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39*** [1.29,1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36*** [1.28,1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37*** [1.31,1.44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90*** [0.86,0.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s schooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16 [0.99,1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12** [1.04,1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14*** [1.06,1.22]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37*** [1.21,1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29*** [1.21,1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.32*** [1.25,1.39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49*** [1.30,1.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44*** [1.33,1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.43*** [1.33,1.52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy Problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 [0.97,1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09*** [1.04,1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07*** [1.03,1.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize of the child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBigger than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11** [1.03,1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24*** [1.18,1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.20*** [1.16,1.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23*** [1.12,1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16*** [1.09,1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18*** [1.12,1.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegistered with\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot ANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01 [0.95,1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94** [0.90,0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96* [0.93,0.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21*** [3.02,3.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.20*** [5.93,6.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.02*** [4.84,5.20]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth order \u0026amp; Birth interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst birth order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2/3 BO \u0026amp; \u0026lt;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71*** [0.64,0.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66*** [0.62,0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67*** [0.64,0.71]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2/3 BO \u0026amp; \u0026gt; 24months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76*** [0.71,0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77*** [0.73,0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77*** [0.74,0.79]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;BO \u0026amp; \u0026lt; 24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77* [0.62,0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63*** [0.55,0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65*** [0.59,0.73]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;BO \u0026amp; \u0026gt;24 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38*** [0.32,0.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39*** [0.35,0.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39*** [0.36,0.42]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24 [0.99,1.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33*** [1.25,1.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.36*** [1.28,1.44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54*** [1.25,1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.61*** [1.51,1.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.68*** [1.59,1.79]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.85*** [1.50,2.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70*** [1.59,1.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.86*** [1.75,1.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83*** [1.48,2.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74*** [1.60,1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.85*** [1.72,1.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88*** [0.82,0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89*** [0.84,0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90*** [0.85,0.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC/ST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98 [0.91,1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10*** [1.05,1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06** [1.02,1.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08 [1.00,1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22*** [1.15,1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17*** [1.12,1.23]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographic region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79 [0.39,1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52 [0.24,1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62 [0.29,1.29]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 [0.61,2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09 [0.56,2.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15 [0.61,2.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 [0.58,1.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87 [0.47,1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96 [0.55,1.69]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 [0.47,1.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56 [0.28,1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70 [0.37,1.35]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.73 [0.97,3.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.92* [1.04,3.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.76 [0.99,3.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of ANC visits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 3 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 4 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08* [1.01,1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15*** [1.10,1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12*** [1.08,1.16]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at marriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13** [1.03,1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13*** [1.07,1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13*** [1.08,1.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37*** [1.22,1.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44*** [1.33,1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.41*** [1.32,1.51]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver Pregnancy Termination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25*** [1.17,1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20*** [1.14,1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.22*** [1.17,1.27]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eState, District and PSU level Variations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariance for State\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 [0.31 0.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25 [0.15 0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 [0.62 1.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3 [0.18 0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89 [0.55 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27 [0.17 0.45]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariance for Districts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19 [0.15 0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11 [0.08 0.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35 [0.3 0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14 [0.11 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35 [0.31 0.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12 [0.11 0.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariance for PSU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 [0.31 0.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18 [0.14 0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44 [0.39 0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25 [0.21 0.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54 [0.49 0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24 [0.21 0.28]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLog-Likelihood\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-22502.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-17113.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-58070.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-41832.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-81202.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-59091.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45015.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34294.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116150.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83732.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162414.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e118252.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45058.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34577.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116199.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84056.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162464.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e118595.1\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\u003eThe Fairlie decomposition shows that 93.76% of the urban\u0026ndash;rural difference in caesarean section delivery is explained by observed characteristics, while 6.24% remains unexplained (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The explained component is driven primarily by maternal characteristics, with place of delivery emerging as the largest contributor, accounting for 49.86% of the explained gap. Other maternal factors such as age at marriage (6.09%), mother\u0026rsquo;s schooling (5.50%), and mother\u0026rsquo;s age at childbirth (4.60%), make moderate contributions to the explained difference. Household and community characteristics also play a substantial role, particularly wealth index (15.74%) and geographic region (8.25%), indicating that socioeconomic position and regional context significantly shape urban\u0026ndash;rural disparities in caesarean delivery. In contrast, some factors such as ANC visits, pregnancy complications, and ANM registration explain only small portions of the disparity.\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\u003eFairlie decomposition analysis of the rural\u0026ndash;urban disparity in caesarean section deliveries by child, maternal, and household characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackground Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% contribution explained\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of the child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order \u0026amp; Birth interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at marriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s age at child birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy Problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ANC visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegistered with ANM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver Pregnancy Termination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold and Community characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExplained\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e93.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnexplained\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.24\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\u003eThis study, utilizing nationally representative data from NFHS-5 (2019\u0026ndash;21), reveals significant urban\u0026ndash;rural disparities in caesarean section (CS) delivery in India, with an overall prevalence of 26.6%. While CS rates in urban areas (36.6%) substantially exceed WHO-recommended thresholds (10\u0026ndash;15%), rural areas (22.3%) reflect a more moderate but rising trend. Our findings reinforce the persistent inequality in obstetric care access and utilization across India, underscoring the dual challenges of CS overuse in urban private facilities and potential underuse in rural public settings, a pattern consistent with the \u0026ldquo;too much, too soon\u0026rdquo; versus \u0026ldquo;too little, too late\u0026rdquo; framework in maternal healthcare. The third Sustainable Development Goal (SDG 3) seeks to ensure healthy lives and promote well-being for all people of all ages, including maternal health. However, safe motherhood and maternal mortality reduction are critical components of SDG 3 (Hak et al., 2016).\u003c/p\u003e \u003cp\u003eConsistent with prior evidence, CS delivery was more common among women who were older, more educated, and from wealthier households (Ghos S, 2010; Kambo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mishra et al., 2002; Padmadas et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Advanced maternal age was associated with a higher likelihood of CS, possibly reflecting increased obstetric risk and provider caution (Richard et al., 2016; Rydahl et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, higher education and wealth may influence delivery mode through greater healthcare access, preferences for medical interventions, and higher utilization of private facilities (Betran et al., 2016; Chu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gebremedhin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These patterns underscore the role of socioeconomic gradients in shaping obstetric decision-making and highlight the need for equity-focused maternal health strategies.\u003c/p\u003e \u003cp\u003eThe study also found that CS likelihood varied by reproductive factors, religion, and antenatal care utilization. Women receiving more ANC visits had a higher probability of CS, a finding that may reflect closer medical surveillance, differential provider practices, or variations in care quality across settings (Divyamol et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yaya et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, lower CS rates among certain religious groups suggest the influence of cultural norms and beliefs, warranting further qualitative investigation (Ghos S, 2010). Taken together, these findings emphasize the importance of strengthening rural public obstetric services while regulating urban private healthcare, promoting evidence-based guidelines, and ensuring that CS is used appropriately to safeguard maternal and neonatal health.\u003c/p\u003e \u003cp\u003eThe urban-rural disparity in Cesarean Section (CS) rates is a significant public health concern, with Fairlie decomposition analysis revealing that 93.8% of this gap is attributable to observable characteristics, primarily the place of delivery, which accounts for 49.9% of the explained disparity (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This substantial contribution from the place of delivery is consistent across various studies examining socioeconomic inequalities in CS utilization (Samuel et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Panda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Deliveries in private hospitals are strongly associated with markedly higher odds of CS, with an Adjusted Odds Ratio (AOR) of 5.02. Decomposition analysis also shows that with place of delivery emerging as the largest contributor, accounting for 49.86% of the explained gap. This finding is corroborated by research from various regions, including India, where private facilities contribute significantly to the rising CS rate (Panda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sk, R. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bhatia 2020). For instance, a study in West Bengal, India, highlighted that women delivering in private facilities had higher odds of undergoing a CS compared to those in public facilities (Sarkar S. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, in Nepal, a substantial rise in CS rates has been observed in private institutions (Bhandari et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This pattern suggests that financial incentives, defensive medicine practices, and patient preferences for \"controlled\" deliveries, particularly among educated and affluent urban women, may contribute to an increase in medically unnecessary CS in private settings (Tuner et al., 2020; Panda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kumar \u0026amp; Lakhtakia \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The presence of financial incentives has been shown to influence healthcare professionals' decisions, though the impact can vary. The global trend also indicates that CS rates in private hospitals often exceed those in public facilities, even after adjusting for clinical indications (Tuner et al., 2020; Ahmad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, rural women's greater reliance on public facilities, often compounded by infrastructural and human resource deficiencies, likely leads to the underutilization of medically indicated CS, thereby reflecting systemic inequities in access to emergency obstetric care (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Abdulla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bobo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies from Sub-Saharan Africa and Bangladesh demonstrate significant urban-rural disparities in the utilization of maternal health services, including access to health facilities for delivery and professional assistance, with rural areas often lagging behind (Samuel at al., 2021; Abdulla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Socioeconomic gradients further stratify CS access. Higher maternal education, greater wealth quintile, and higher social status (e.g., OBC/Others versus Scheduled Castes/Scheduled Tribes, or SC/ST) are consistently associated with an increased likelihood of CS. These disparities align with findings from various low- and middle-income countries, including those in Sub-Saharan Africa and South Asia (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shibre et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wyatt et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Maternal factors significantly contribute to urban-rural disparities in Cesarean Section (CS) rates, with age at marriage (6.09%), mother's schooling (5.50%), and mother's age at childbirth (4.60%) identified as moderate contributors to the explained difference (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings align with broader observations that maternal demographic and educational attributes influence health-seeking behaviors and healthcare utilization (Zahroh et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Abdulla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, household and community characteristics play a substantial role, notably the wealth index (15.74%) and geographic region (8.25%) (Abate et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This underscores that socioeconomic position and regional context are critical in shaping urban-rural CS disparities, a pattern consistently reported across low- and middle-income countries where wealth and residential location dictate access to quality maternal healthcare (Samuel at al., 2021; Yaya et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, factors like (ANC) visits, pregnancy complications, and Auxiliary Nurse Midwife (ANM) registration explain only small portions of the overall urban-rural CS disparity. This suggests that while these clinical and care-seeking behaviours are important, their influence on the urban-rural CS gap is less pronounced compared to fundamental socioeconomic and demographic determinants. The limited contribution of ANC visits, for example, could indicate that while essential for maternal health, their impact on the mode of delivery within differing urban and rural infrastructures varies (Abate at al., 2025).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCS delivery in India is profoundly shaped by place of residence, healthcare sector, and socioeconomic advantage. The urban\u0026ndash;private nexus drives CS overuse, while rural\u0026ndash;public settings may inadequately provide life-saving surgical care. Achieving equitable, evidence-based maternity care requires targeted policy action that regulates private-sector excess, fortifies public-sector capacity, and prioritizes maternal well-being over commercial or convenience-driven practices. Such efforts are essential not only for improving birth outcomes but also for advancing progress toward Sustainable Development Goal 3 ensuring healthy lives and well-being for all.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eThis study has several limitations. The cross-sectional design precludes causal inference. Self-reported data may be subject to recall and social desirability biases. Lack of clinical indications for CS limits assessment of appropriateness. Furthermore, unobserved confounders such as provider characteristics, hospital policies, and maternal request could influence CS outcomes. Future research should incorporate mixed-methods approaches to explore provider motivations, patient preferences, and context-specific barriers. Longitudinal studies tracking CS trajectories and linked clinical-data analyses would enhance understanding of medical necessity versus discretionary practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePolicy Implications\u003c/h2\u003e \u003cp\u003eOur findings highlight an urgent need for dual-focused interventions: curbing non-medically indicated CS in urban-private facilities while ensuring timely access to emergency obstetric surgery in rural-public settings. Regulatory measures including CS audits, evidence-based clinical guidelines, and transparent billing in private hospitals could mitigate supplier-induced demand. Concurrently, strengthening rural emergency obstetric care through staff training, infrastructure upgradation, and demand-side financing (e.g., conditional cash transfers) may address underuse. Promoting midwifery-led care and vaginal birth after caesarean (VBAC) in both sectors could further rationalize CS rates. Additionally, community awareness programs addressing CS risks and benefits may empower informed decision-making, particularly among less-educated and rural women.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003ePatient and Public Involvement\u003c/strong\u003e \u003cp\u003ePatients and the public were not involved in the design, conduct, reporting, or dissemination of this research. This study used secondary anonymised data from the National Family Health Survey (NFHS-5, 2019\u0026ndash;21).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eEthics approval and consent to participate\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eThis study is based on secondary analysis of anonymised data from the National Family Health Survey (NFHS-5), which is publicly available upon reasonable request from the DHS Program. Ethical approval for the original survey was obtained by the implementing agencies, and no additional ethical approval was required for this secondary analysis.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePP, MD, MS conceptualised the study, and MS, PP, conducted all the data analyses, HK, MD, PP, MS analysed the results and drafted the manuscript. HK and MS had done the critical revision. All the authors read and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWe used open-source secondary data, easily available on request [https://dhsprogram.com/data/available-datasets.cfm](https:/dhsprogram.com/data/available-datasets.cfm) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbate BJ, Gedefaw GD, Asmare TB, Demissie B, Gobezie NZ, Wubet HB, Adeleye K. (2025). Prevalence, urban-rural disparity and determinants of caesarean section delivery among women delivering at health facilities in 48 low-and middle-income countries: a multilevel and decomposition analysis. BMJ Global Health, 10(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdulla F, Hossain MM, Rahman MM, Rahman MS, Rahman A. Risk factors of caesarean deliveries in urban\u0026ndash;rural areas of Bangladesh. Front Reproductive Health. 2023;5:1101400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad F, Meyer E, Higgs M. Financial incentives and prosocial motivation among physicians in a faith-based society. Public Perform Manage Rev. 2024;47(5):1302\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnanna MN, Hossain MB. Caesarean Delivery-a Pressured Choice for Women: A Meta-Analysis of 23 Cross-Sectional Studies. Dhaka Univ J Sci. 2023;71(1):1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArendt F, Markiewitz A, Mestas M, Scherr S. COVID-19 pandemic, government responses, and public mental health: Investigating consequences through crisis hotline calls in two countries. Soc Sci Med. 2020;265:113532.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBetr\u0026aacute;n AP, Ye J, Moller AB, Zhang J, G\u0026uuml;lmezoglu AM, Torloni MR. (2016). The increasing trend in caesarean section rates: global, regional and national estimates: 1990\u0026ndash;2014. PLoS ONE, 11(2), e0148343.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhandari AK, Dhungel B, Rahman M. Trends and correlates of cesarean section rates over two decades in Nepal. BMC Pregnancy Childbirth. 2020;20(1):763.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhartia A, Sen Gupta Dhar R, Bhartia S. Reducing caesarean section rate in an urban hospital serving women attending privately in India\u0026ndash;a quality improvement initiative. BMC Pregnancy Childbirth. 2020;20(1):556.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatia M, Banerjee K, Dixit P, Dwivedi LK. Assessment of variation in cesarean delivery rates between public and private health facilities in India from 2005 to 2016. JAMA Netw open. 2020;3(8):e2015022\u0026ndash;2015022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatnagar DM, Jharbade DH, Porwal DS. (2023). A RETROSPECTIVE STUDY OF PREVALENCE AND MAJOR INDICATIONS OF CESAREAN SECTION IN A TERTIARY CARE HOSPITAL IN INDIA.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlinder AS. (1973). Wage discrimination: reduced form and structural estimates. J Hum Resour, 436\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBobo FT, Asante A, Woldie M, Dawson A, Hayen A. (2021). Spatial patterns and inequalities in skilled birth attendance and caesarean delivery in sub-Saharan Africa. BMJ Global Health, 6(10), e007074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai WW, Marks JS, Chen CH, Zhuang YX, Morris L, Harris JR. Increased cesarean section rates and emerging patterns of health insurance in Shanghai, China. Am J Public Health. 1998;88(5):777\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaurasia AR. (2022). Urban-Rural Disparity in Family Planning Use in India, 1992\u0026ndash;2021. Available at SSRN 4110330.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu K, Cortier H, Maldonado F, Mashant T, Ford N, Trelles M. Cesarean section rates and indications in sub-Saharan Africa. a multi-country study from Medecins sans Frontieres; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarnal N, Dangal G. Maternal and fetal outcome in emergency versus elective caesarean section. J Nepal Health Res Counc. 2020;18(02):186\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDivyamol N, Raphael L, Koshy N. Caesarean section rate and its determinants in a rural area of South India. Int J Community Med Public Health. 2016;3(10):2836\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFairlie RW. An extension of the Blinder-Oaxaca decomposition technique to logit and probit models. J Econ Soc Meas. 2005;30(4):305\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarson GD. Hierarchical linear modeling: Guide and applications. Sage; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebremedhin S. Trend and socio-demographic differentials of Caesarean section rate in Addis Ababa, Ethiopia: analysis based on Ethiopia demographic and health surveys data. Reproductive health. 2014;11(1):14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein H. Multilevel statistical models. Wiley; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGondwe T, Betha K, Kusneniwar GN, Bunker CH, Tang G, Simhan H, Haggerty CL. Adverse infant outcomes associated with caesarean section delivery in India. Int Health. 2020;12(5):411\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovil D, Mohanty SK, Narzary PK. Catastrophic household expenditure on caesarean deliveries in India. J Popul Res. 2020;37(2):139\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGregory KD, Jackson S, Korst L, Fridman M. Cesarean versus vaginal delivery: whose risks? Whose benefits? Am J Perinatol. 2012;29(01):07\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026aacute;k T, Janouškov\u0026aacute; S, Moldan B. Sustainable Development Goals: A need for relevant indicators. Ecol Ind. 2016;60:565\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHlavac M. (2014). oaxaca: Blinder-Oaxaca decomposition in R. Available at SSRN 2528391.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences (IIPS), \u0026amp; ICF. National Family Health Survey (NFHS-5), 2019\u0026ndash;21: India. Mumbai, India: IIPS; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences (IIPS), \u0026amp; Macro International. National Family Health Survey (NFHS-3), 2005\u0026ndash;06: India. Volume 1. Mumbai, India: IIPS; 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKambo I, Bedi N, Dhillon BS, Saxena NC. A critical appraisal of cesarean section rates at teaching hospitals in India. Int J Gynecol Obstet. 2002;79(2):151\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Lakhtakia S. Rising cesarean deliveries in India: medical compulsions or convenience of the affluent? Health Care Women Int. 2021;42(4\u0026ndash;6):611\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumbiganon P, Laopaiboon M, G\u0026uuml;lmezoglu AM, Souza JP, Taneepanichskul S, Ruyan P, Villar J. Method of delivery and pregnancy outcomes in Asia: the WHO global survey on maternal and perinatal health 2007\u0026ndash;08. Lancet. 2010;375(9713):490\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra US, Ramanathan M. Delivery-related complications and determinants of caesarean section rates in India. Health Policy Plann. 2002;17(1):90\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMose A, Abebe H. Magnitude and associated factors of caesarean section deliveries among women who gave birth in Southwest Ethiopia: institutional-based cross-sectional study. Archives Public Health. 2021;79(1):158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOaxaca R. (1973). Male-female wage differentials in urban labor markets. Int Econ Rev, 693\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOinam J, Kongjenbam S, Singh YN. (2020). Prevalence of caesarean section and womens' attitude towards caesarean section in Manipur, North-Eastern India.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadmadas SS, Nair SB, KR AK. Caesarean section delivery in Kerala, India: evidence from a national family health survey. Soc Sci Med. 2000;51(4):511\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanda BK, Nayak I, Mishra US. Determinant of inequality in cesarean delivery in India: a decomposition analysis. Health Care Women Int. 2020;41(7):817\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotter JE, Berqu\u0026oacute; E, Perp\u0026eacute;tuo IH, Leal OF, Hopkins K, Souza MR, de Carvalho Formiga MC. Unwanted caesarean sections among public and private patients in Brazil: prospective study. BMJ. 2001;323(7322):1155\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowers DA, Yoshioka H, Yun MS. mvdcmp: Multivariate decomposition for nonlinear response models. Stata J. 2011;11(4):556\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRai SD, Poobalan A, Jan R, Bogren M, Wood J, Dangal G, Shahid F. Caesarean Section rates in South Asian cities: Can midwifery help stem the rise? Journal of Asian Midwives (JAM); 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards MK, Flanagan MR, Littman AJ, Burke AK, Callegari LS. Primary cesarean section and adverse delivery outcomes among women of very advanced maternal age. J Perinatol. 2016;36(4):272\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRydahl E, Declercq E, Juhl M, Maimburg RD. (2019). Cesarean section on a rise\u0026mdash;Does advanced maternal age explain the increase? A population register-based study. PLoS ONE, 14(1), e0210655.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahoo H, Jeermison RK. Repeated caesarean section delivery in India. Child Youth Serv Rev. 2020;116:105258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuel O, Zewotir T, North D. Decomposing the urban\u0026ndash;rural inequalities in the utilisation of maternal health care services: evidence from 27 selected countries in Sub-Saharan Africa. Reproductive Health. 2021;18(1):216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar S. Prevalence and determinants of the use of caesarean section (CS) in the dichotomy of \u0026lsquo;public\u0026rsquo;and \u0026lsquo;private\u0026rsquo;health facilities in West Bengal. India. Clin Epidemiol Global Health. 2020;8(4):1377\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar S. Prevalence and determinants of the use of caesarean section (CS) in the dichotomy of \u0026lsquo;public\u0026rsquo;and \u0026lsquo;private\u0026rsquo;health facilities in West Bengal. India. Clin Epidemiol Global Health. 2020;8(4):1377\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Jaiswal AK, Singh RK, Kumar P, Mehra S. Differential access to facilities for medical termination of pregnancy and delivery in India: a secondary analysis. Clin Epidemiol Global Health. 2021;12:100825.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShibre G, Zegeye B, Ahinkorah BO, Keetile M, Yaya S. Magnitude and trends in socio-economic and geographic inequality in access to birth by cesarean section in Tanzania: evidence from five rounds of Tanzania demographic and health surveys (1996\u0026ndash;2015). Archives Public Health. 2020;78(1):80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShukla M, Mohan M, van Duinen A, Gadgil A, Bakker J, Bhushan P, Roy N. (2022). Assessing geographical and economic inequalities in caesarean section rates between the districts of Bihar, India: a secondary analysis of the National Family Health Survey. BMJ open, 12(1), e055326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimmons E, Lane K, Rao SR, Kurhe K, Patel A, Hibberd PL. (2021). Trends in cesarean section rates in private and public facilities in rural eastern Maharashtra, India from 2010\u0026ndash;2017. PLoS ONE, 16(8), e0256096.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh SK, Aditi, Sharma SK. Changing the discourse on caesarean births in India: issues emerging from NFHS-5 (2019\u0026ndash;2021). SN Social Sci. 2022;2(7):103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh SK, Vishwakarma D, Sharma SK. Prevalence and determinants of voluntary caesarean deliveries and socioeconomic inequalities in India: Evidence from National Family Health Survey (2015-16). Clin Epidemiol Global Health. 2020;8(2):335\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSk R. (2020). Does delivery in private hospitals contribute largely to Caesarean Section births? A path analysis using generalised structural equation modelling. PLoS ONE, 15(10), e0239649.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSufang G, Padmadas SS, Fengmin Z, Brown JJ, Stones RW. Delivery settings and caesarean section rates in China. Bull World Health Organ. 2007;85:755\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyatt S, Silitonga PII, Febriani E, Long Q. Socioeconomic, geographic and health system factors associated with rising C-section rate in Indonesia: a cross-sectional study using the Indonesian demographic and health surveys from 1998 to 2017. BMJ open. 2021;11(5):e045592.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaya S, Bishwajit G, Shah V. (2016). Wealth, education and urban\u0026ndash;rural inequality and maternal healthcare service usage in Malawi. BMJ global health, 1(2), e000085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaya S, Uthman OA, Amouzou A, Bishwajit G. Disparities in caesarean section prevalence and determinants across sub-Saharan Africa countries. Global health Res policy. 2018;3(1):19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaya S, Zegeye B, Idriss-Wheeler D, Shibre G. Inequalities in caesarean section in Burundi: evidence from the Burundi demographic and health surveys (2010\u0026ndash;2016). BMC Health Serv Res. 2020;20(1):652.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahroh RI, Disney G, Betr\u0026aacute;n AP, Bohren MA. (2020). Trends and sociodemographic inequalities in the use of caesarean section in Indonesia, 1987\u0026ndash;2017. BMJ global health, 5(12).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Caesarean section, Urban–rural disparities, Multilevel logistic regression, Maternal health services, Private healthcare, NFHS-5, India","lastPublishedDoi":"10.21203/rs.3.rs-9229990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9229990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eCaesarean section (C-section) delivery is a critical maternal health intervention when medically indicated; however, its rapid increase beyond recommended levels (range of 10\u0026ndash;15%) raises concerns about unnecessary surgical births. India has witnessed a substantial rise in C-section deliveries, with pronounced urban\u0026ndash;rural differentials, reflecting inequalities in healthcare access, service provision, and socioeconomic status. Understanding how place of residence interacts with demographic and socioeconomic factors is essential for addressing inequities in delivery care.\u003c/p\u003e\u003ch2\u003eData and Methods:\u003c/h2\u003e \u003cp\u003eThis study used data from the National Family Health Survey (NFHS-5, 2019\u0026ndash;21), covering 155,624 most recent institutional births among currently married women aged 15\u0026ndash;49 years in India. Descriptive and bivariate analyses were followed by stratified (urban, rural and total) multilevel logistic regression models (individuals nested within PSUs, districts, and states) to assess contextual variation and determinants. Model fit was evaluated using AIC, BIC, and intra-class correlation coefficients (ICCs). Furthermore, to understand the factors associated with differential pattern in Caesarean delivery in rural and urban Fairlie decomposition technique has been applied.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 26.6% of deliveries occurred by C-section, with a substantially higher prevalence in urban areas (36.6%) compared to rural areas (22.3%). Multilevel analysis showed significant clustering at PSU, district, and state levels. Deliveries in private hospitals had markedly higher odds of C-section (AOR\u0026thinsp;=\u0026thinsp;5.02; 95% CI: 4.84\u0026ndash;5.20). Higher maternal age, education, wealth status, \u0026ge;\u0026thinsp;4 ANC visits, and pregnancy complications increased the likelihood of C-section, while higher birth order and longer birth intervals reduced it. Even after adjustment, rural residence remained associated with lower odds of C-section. Fairlie decomposition analysis revealed that 93.8% of the urban\u0026ndash;rural gap in caesarean section deliveries is explained by observed characteristics, with maternal factors contributing the largest share. Place of delivery alone accounted for nearly half of the explained disparity (49.9%), followed by household wealth (15.7%) and region (8.3%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eC-section delivery in India is strongly patterned by place of residence, healthcare sector, and socioeconomic advantage. The findings highlight growing urban\u0026ndash;private sector dominance in C-section use and persistent contextual inequalities. Policy efforts should focus on regulating private facilities, improving quality of obstetric care in rural areas, and promoting evidence-based delivery practices to ensure medically appropriate and equitable maternal healthcare.\u003c/p\u003e","manuscriptTitle":"Quantifying Urban–Rural Disparities in Caesarean Section Deliveries in India: A Multilevel and Fairlie Decomposition Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 16:05:49","doi":"10.21203/rs.3.rs-9229990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-07T15:28:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T23:21:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T23:21:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reproductive Health","date":"2026-03-26T06:18:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10637e02-97e9-4683-a362-ee74384df2a6","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T16:05:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 16:05:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9229990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9229990","identity":"rs-9229990","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}