Determinants and Spatial Variation of Skilled Birth Assistance in Nepal: Insights from NDHS 2022

Determinants and Spatial Variation of Skilled Birth Assistance in Nepal: Insights from NDHS 2022 | 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 Determinants and Spatial Variation of Skilled Birth Assistance in Nepal: Insights from NDHS 2022 Md Salek Miah, Mst. Labonna Begum, Fhameda Faija Lamia, Md. Yousuf Ali, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7973803/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background : Skilled birth assistance (SBA) is one of the determinants of maternal and neonatal health. Although there has been development in Nepal, there is still imbalance in SBA coverage in socioeconomic, obstetric, and geographical dimensions. This research sought to determine the major risk factors of SBA, spatial differences in SBA, and rural and urban differences in the determinants. Methodology : This study used national representative data on Nepal Demographic and Health Survey (NDHS) 2022, which included women age 15-49 years with a last live birth within the last 5 years. The outcome variable categorized as SBA at delivery (skilled vs unskilled). Step wise survey-weighted multivariable logistic regression was used to assess the national and rural -urban determinants with fitted assessed by likelihood ratio tests, (AIC), (BIC), and result presented as adjusted odds ratios (aORs) with the 95 % confidence intervals (CI). Findings : Among 2,785 women, 72.5% of women were received skill birth assistance (SBA). The uptake of SBA was highest among women aged 20-34 years, secondary or higher education, and richer households. Multiparity (aOR :0.47 ,95% CI:0.36–0.63). The obstetric variables such as 4 or more antenatal care visits (aOR 2.30, 95% CI 1.80-2.94) and caesarean section delivery (aOR: 3.86, 95% CI: 2.42-6.16) were significant predictors of SBA. Maternal age during first birth 20 years and above, increased SBA likelihood to a moderate extent (aOR :1.23,95% CI 1.00-1.50). SBA coverage in the provinces differed significantly with the highest level in Madhesh (≈18 .19%) and the smallest in Gandaki and Bagmati (≈9.11%). Caesarean delivery, ≥4 ANC visits, and residence in Lumbini were the most significant predictors in rural areas and Gandaki residence, caesarean delivery, ≥4 ANC visits and no desire of having additional children were the most significant predictors in urban areas. Conclusion : Maternal, obstetric and socioeconomic factors have a strong impact on SBA in Nepal, and there are immense geographic and rural-urban differences. Specific actions taken against ANC uptake, parity, and provincial inequity: It is necessary to increase SBA coverage on the national level. Spatial Stratified Determinants SBA Figures Figure 1 Figure 2 Figure 3 Introduction There have been significant changes in maternal health in the past decades, maternal mortality continues to be a significant issue in the population, especially in low- and middle-income nations (LMICs) [ 1 ]. Sustainable Development Goal (SDG) 3 seeks to lower the maternal mortality rate (MMR) to less than 70 deaths per 100,000 live births across the world by 2030 [ 2 , 3 ]. But the development towards this purpose has been unequal[ 4 ]. LMICs continue to experience more than 90 percent of maternal mortality, and South Asia alone constitutes almost a fifth [ 5 ]. Majority of these deaths are avoidable and occur as a result of complications during pregnancy, labor or a few days after birth[ 6 ]. It is thus a vital requirement that maternal health services be available to address preventable maternal deaths[ 7 ]. Skilled Birth Attendance (SBA) is one of the most effective methods of reducing both maternal and neonatal morbidity and mortality as Skilled Birth Attended (SBA) has become one of the essential maternal health services [ 8 ]. Well trained attendants will be able to diagnose and treat obstetric complications, give timely referrals and safer delivery among mothers and babies [ 9 ]. Nevertheless, there are still inequalities in SBA coverage both across as well as within countries even with robust policy commitments and investment in the health system, especially in the rural and disadvantaged areas [ 10 ]. The extent of the use of SBA in South Asia is quite different among the countries. In 2016, 58 percent of births in Nepal were served by skilled personnel[ 11 ], 81 percent in India[ 12 ]. As has been proven by evidence provided in LMICs, the usage of SBA is predetermined by a complex of individual, house, and situational factors [ 13 ]. Research in other nations like Guinea, Kenya, and Tanzania have revealed that maternal education, household wealth, parity, antenatal care (ANC) visits, and the closeness to health facilities are the main predictors of SBA use [ 4 , 14 , 15 ]. Likewise, a study in India and Ethiopia has determined that place of residence, wealth, and media exposure have a significant impact on the uptake of services [ 16 ]. Past research has also pointed out parity, maternal education, preparedness to birth, and distance to health facilities in Nepal as key determinants [ 17 ].Nevertheless, a majority of such studies had been restricted either to a particular province or to narrow sample sizes and little represented the country wide spatial and rural-urban differences [ 18 ]. Although the country is better covered nationally, there is a lack of evidence regarding spatial distribution and rural-urban differences in SBA utilization in Nepal regarding recent nationally representative data [ 19 ]. Furthermore, past analyses have tended to have incomplete model selection processes that could help to determine the most powerful predictors of skilled delivery care [ 20 , 21 ]. These gaps need to be filled in so as to have a better grasp of the women and areas left behind. Thus, the following research questions are addressed by the study: (1) to determine the prevalence and spatial variation of skilled birth attendance among the ever-married women aged 15–49 years in Nepal; (2) the identification of individual- and community-level predictors of SBA through a stepwise model selection strategy to provide the methodological strength of the study; and (3) investigate the rural to urban differences in the predictors. The results will inspire policymakers and general health practitioners to develop more specific, balanced approaches to alleviate skilled delivery coverage and to enhance the development of Nepal that is focused towards meeting SDG 3 by 2030. Methodology Study Design and Setting This study used secondary data from the 2022 Nepal Demographic and Health Survey (NDHS), cross-sectional study conducted by the Ministry of Health and Population (MoHP) between January 5 and June 22, 2022, in collaboration with New ERA and ICF under the global DHS Program. Nepal is a landlocked South Asian country bordered by China and India and is administratively divided into seven provinces and 77 districts across three ecological zones: Mountain, Hill, and Terai. The NDHS 2022 employed a two-stage stratified cluster sampling design to ensure national representativeness. In the initial stage, 383 enumeration areas (112 urban and 271 rural) were selected with probability proportional to size based on the 2021 National Population and Housing Census. In the last stage, 30 households were systematically selected from each EA using updated household listings [ 22 ]. Study Population and Sample The study population included ever-married women aged 15–49 years who had given birth within five years preceding the survey. After applying inclusion criteria and sampling weights, a total of 2,785 women were analyzed [ see in Supplementary Figure S1 ].. Outcome variable The outcome variable was skilled birth attendance (SBA) , defined as delivery assisted by a qualified health professional such as a doctor, nurse, or midwife. Deliveries attended by these providers were coded as “1 = skilled,” while deliveries attended by traditional birth attendants, relatives, or others were coded as “0 = unskilled.” Covariates Independent variables included maternal age, education, husband’s education, household wealth, parity, ANC visits, employment status, media status, decision-making autonomy, place of residence, province, and province. Data Management Data were cleaned, recoded, and verified against the NDHS 2022 codebook. Sampling weights (v005), primary sampling units (v021), and stratification variables (v023) were applied by the svyset command in STATA 17 to account for the complex survey design, and R studio 4.5.1 used for spatial analysis. This study maintains STROBE guidelines. Statistical Analysis and Model Building Descriptive statistics were used to summarize participants’ characteristics and SBA prevalence. Bivariate associations were assessed using chi-square tests. Multivariable logistic regression was conducted using a stepwise selection approach to detect risk factors of SBA utilization. Eighteen sequential models were fitted, and the 16th model was selected as the final model based on lowest AIC (2877.235) and BIC (3031.468) values. This model included key confounders, including maternal age, education, husband’s education, household wealth, parity, ANC visits, employment status, media exposure, and residence-related factors. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Model diagnostics For checking multicollinearity using variance inflation factors (VIF = 1.87) assessing model calibration with the Hosmer Lemeshow goodness-of-fit test, and evaluating discriminative ability used Receiver Operating Characteristic curve (AUCROC). Ethical Considerations The NDHS 2022 received ethical approval from the Nepal Health Research Council (NHRC) and the ICF Institutional Review Board . Written informed consent was obtained from all participants prior to interviews. Permission to use the anonymized dataset for secondary analysis was granted by the DHS Program , and details available at The DHS Program - Protecting the Privacy of DHS Survey Respondents. Results Most deliveries occurred among women aged 20–34 years (≈ 83%), with slightly higher unskilled births among women > 35 years. Skilled births were more common among Hindu women (≈ 86%) and those with at least basic education and from middle-to-rich households, whereas unskilled deliveries predominated among poorer, less-educated women. Regions with lower prevalence of skilled births were seen in Madhesh and Sudurpashchim provinces, which points to unfair distributions in maternal care access in the regions [Figure 1 ]. All in all, trained professionals attended to 72.5% of the births, and the unprofessional attendants took care of 27.5% of them. The age of the mother slightly related to skilled birth assistance (p = 0.056). Women who received skilled care was more among the 20–34 years (60.14%) than the < 20 years (7.72%) and the over 35 years (4.63%). Religion had marginal significant difference (p = 0.059) whereby, Hindu mothers were more likely to utilize skilled birth services as compared to non-Hindu women. Maternal age at first birth (p < 0.001) and age at first sex (p < 0.001) were also significant. Other significant factors were parity (p < 0.001), number of children (p < 0.001); primiparous mothers and single-child mothers had a higher possibility of seeking skilled care. There was a significant association with frequent antenatal care (4 visits and above, p < 0.001) and caesarean delivery (p < 0.001). An evident wealth gradient was presented (p < 0.001): 22.98% of women in the rich population had been presented with skilled help in contrast to 35.66% of the poor. Skilled attendance was significantly associated with the living in urban residence (p < 0.001) and possessing health insurance (p < 0.001). The employment status (p = 0.006) also determined utilization, working women had better skilled attendance (48.04%) when compared to non-working women (24.45%). The education level of both husband and woman was a significant risk factor (both p < 0.001); the rate of skilled assistance rose as the level of education improved. Females who were exposed to mass media (p < 0.001) and those that used internet regularly (p < 0.001) were considerably more likely to receive skilled assistance. The inequality across the regions was significant (p < 0.001). The largest percentages of skilled birth attendance were reported in Bagmati and Gandaki provinces and the lowest percentage was reported in Karnali and Sudurpashchim [Table 1 ]. Table 1 Distribution of skilled and unskilled births by demographic, socioeconomic, obstetric, and contextual variables in Nepal (NDHS 2022) Variables Skilled Unskilled Total n (%) P-value n (%) n (%) Outcome Skilled Birth Assistance 2,019 (72.5) 766 (27.5) 100% Covariates Demographic Variables Maternal Age 0.056 35 129 (4.63) 65 (2.33) 194(6.97) Religious Status 0.059 Hindu 1,734 (62.26) 636(83.03) 2,370(85.10) Non-Hindu 285 (10.23) 130(4.67) 415(14.90) Ethnicity 0.362 0 745(26.75) 297(10.66) 1,042(37.41) 1 1,274(45.75) 469(16.84) 1,743(62.59) Maternal age at first birth < 0.001 < 20 years 884(31.74) 432(15.37) 1,316(47.25) ≥ 20 years 1,135(40.75) 334(11.99) 1,469(52.75) Age at first sex < 0.001 < 18 years 831(29.84) 428(55.87) 1,259(45.21) ≥ 18 years 1,188(42.66) 338(12.14) 1,526(54.79) Household & Socioeconomic Variables Wealth Status < 0.001 Poor 993 (35.66) 532(19.10) 1,525(54.76) Middle 386 (13.86) 120(4.31) 506(18.17) Rich 640 (22.98) 114(4.09) 754 (27.07) residence 5 member 953(34.22) 385(13.82) 1,338(48.04) Health Insurance No 1,789(64.24) 720(25.85) 2,509(90.09) < 0.001 Yes 230(8.26) 46(1.65) 276(9.91) Working Status 0.006 Not working 681(24.45) 217(7.79) 898(32.24) Working 1,338(48.04) 549(19.71) 1,887(67.76) Husband Education < 0.001 No education 136(4.88) 110(3.95) 246(8.83) Basic 747(26.82) 348(12.50) 1,095(39.32) Secondary 1,096(39.35) 287(10.31) 1,383(49.66) Missing 4.38 Woman Education < 0.001 No Education 290(10.41) 232(8.33) 522(18.74) Basic 1,299(46.64) 467(16.77) 1,766(63.41) High 430(15.44) 67(2.41) 497(17.85) Obstetric & Reproductive Variables Parity < 0.001 Primiparo 923(33.14) 194(6.97) 1,117(40.11) Multiparo 1,096(39.35) 572(20.54) 1,668(59.89) child_cat < 0.001 Single child 959(34.43) 218(7.83) 1,177(42.26) Multiple Child 1,060(38.06) 548(19.68) 1,608(57.74) ANC visit < 0.001 =4 1,781(63.95) 523(18.78) 2,304(82.73) Delivery mode < 0.001 Vaginal delivery 1,610(57.81) 735(26.39) 2,345(84.20) Caesarean 409(14.69) 31(1.11) 440(15.80) Birth size 0.475 Small / U 315(11.31) 128(4.60) 443(15.91) Average 1,704(61.18) 638(22.91) 2,342(84.09) PNC < 0.001 No prenatal 977(35.08) 476(17.09) 1,453(52.17) Prenatal 1,042(37.41) 290(10.41) 1,332(47.83) Pressure to pregnant 0.037 Not pressured 1,863(67.33) 690(24.94) 2,553(92.27) Pressured 142(5.13) 72(2.60) 214(7.73) Sexual Activity 0.448 Not active 911(32.77) 333(11.98) 1,244(44.75) Active 1,105(39.75) 431(15.50) 1,536(55.25) fert_pref 0.057 Do not Want 1,220(43.81) 493(17.70) 1,713(61.51) Want More 799(28.69) 273(9.80) 1,072(38.49) genital_ulcer 0.124 No 1,936(69.62) 744(26.75) 2,680(96.37) Yes 80(2.88) 21(0.76) 101(3.63) Media Exposure & Empowerment Variables Mass Media < 0.001 No media 87(3.12) 74(2.66) 161(5.78) Any media 1,932(69.37) 692(24.85) 2,624(94.22) Internet Use < 0.001 No use 929(33.36) 515(18.49) 1,444(51.85) Use regular 1,090(39.14) 251(9.01) 1,341(48.15) Decision making 0.539 No 1,272(45.97) 493(17.82) 1,765(63.79) Yes 733(26.49) 269(9.72) 1,002(36.21) IPV No 1,639(59.04) 609(21.94) 2,248(80.98) Yes 374(13.47) 154(5.55) 528(19.02) Geographic & Contextual Variables Province < 0.001 Koshi 298 (10.70) 126 (4.52) 424 (15.2) Madhesh 375(13.46) 185(6.64) 560(20.11) Bagmati 243(8.73) 89(3.20) 332(11.92) Gandaki 177(6.36) 39(1.40) 216(7.76) Lumbini 319(11.45) 70(2.51) 389(13.97) Karnali 317(11.38) 136(4.88) 453(16.27) Sudurpash 290(10.41) 121(4.34) 411(14.76) Maternal age was not associated with SBA. Women aged 20–34 years (AOR = 0.82; 95% CI: 0.53–1.28) and > 35 years (AOR = 0.83; 95% CI: 0.43–1.61) had comparable probabilities of skilled attendance with women under 20 years old. The educational level of women had a marginal significant positive association: secondary or higher-educated women had 1.43 times higher odds for skilled help compared to illiterate women (AOR = 1.43; 95% CI: 0.99–2.06; p = 0.054). Husband's level of education contributed minimally to SBA use. Household income was a significant determinant. Compared with poor households, middle-income (AOR = 1.60; 95% CI: 1.22–2.11; p = 0.001) and affluent households (AOR = 1.73; 95% CI: 1.25–2.40; p = 0.001) women were more likely to deliver with skilled delivery care. Residence location was nonsignificant (p = 0.523). There were significant provincial variations. Women in Gandaki (AOR = 2.09; 95% CI: 1.15–3.79; p = 0.015), Lumbini (AOR = 1.99; 95% CI: 1.20–3.31; p = 0.008), and Karnali (AOR = 1.60; 95% CI: 1.01–2.54; p = 0.045) provinces had higher odds of skilled attendance compared to women in Province 1, and no variation was observed in other provinces. Parity was negatively associated with SBA use (AOR = 0.47; 95% CI: 0.36–0.63; p < 0.001), indicating that multiparous women were less likely to have skilled attendance compared to primiparous women. Women who had a first birth at ≥ 20 years were more likely to have skilled care (AOR = 1.23; 95% CI: 1.00–1.50; p = 0.047). ANC visit frequency was among the strongest predictors: women who had four or more ANC visits were more than twice as likely to have skilled birth care (AOR = 2.30; 95% CI: 1.80–2.94; p < 0.001). Delivery mode was highly significant as caesarean sections were nearly three times higher skilled birth assistance (AOR = 3.86; 95% CI: 2.42–6.16; p < 0.001). Those women in the need for additional children had significantly reduced odds of receiving skilled care (AOR = 0.66; 95% CI: 0.50–0.86; p = 0.002. But frequent use of the internet was also significant as frequently internet use, women used internet showed a 1.32 times increased probability of getting skilled care (AOR = 1.32; 95% CI: 1.03–1.69; p = 0.027) [Table 2 ]. Table 2 Survey-weighted adjusted odds ratios for determinants of skilled birth attendance (SBA) in Nepal. Variable OR (95% CI) p-value Age 35 0.83 (0.43–1.61) 0.582 Education (Woman) None (ref) – – Basic 1.17 (0.87–1.58) 0.299 Secondary/Higher 1.43 (0.99–2.06) 0.054 Husband’s Education None (ref) – – Basic 1.12 (0.78–1.60) 0.540 Secondary/Higher 1.20 (0.81–1.78) 0.357 Don’t know/Missing 1.06 (0.55–2.03) 0.859 Wealth Poor (ref) – – Middle 1.60 (1.22–2.11) 0.001 Rich 1.73 (1.25–2.40) 0.001 Residence Urban (ref) – – Rural 0.92 (0.72–1.18) 0.523 Province Province1 (ref) – – Madhesh 1.19 (0.77–1.84) 0.439 Bagmati 0.99 (0.60–1.61) 0.957 Gandaki 2.09 (1.15–3.79) 0.015 Lumbini 1.99 (1.20–3.31) 0.008 Karnali 1.60 (1.01–2.54) 0.045 Sudurpashchim 1.37 (0.87–2.16) 0.171 Parity Primiparous (ref) – – Multiparous (2 + births) 0.47 (0.36–0.63) < 0.001 Maternal Age at First Birth <20 (ref) – – ≥20 years 1.23 (1.00–1.50) 0.047 ANC Visits <4 (ref) – – ≥4 2.30 (1.80–2.94) < 0.001 Delivery Mode Vaginal (ref) – – Caesarean section 3.86 (2.42–6.16) < 0.001 PNC Doctor None (ref) – – Prenatal doctor 1.10 (0.87–1.40) 0.419 Fertility Preference Want none (ref) – – Want more 0.66 (0.50–0.86) 0.002 Working Status Not working (ref) – – Working 0.91 (0.70–1.18) 0.455 Media Exposure None (ref) – – Any 0.86 (0.57–1.28) 0.458 Internet Use None (ref) – – Use regular 1.32 (1.03–1.69) 0.027 Age, Education (Woman), Husband’s Education, Wealth, Residence, Province, Parity, Maternal Age at First Birth, ANC Visits, Delivery Mode, PNC Doctor, Fertility Preference, Working Status, Media Exposure, Internet Use In rural women, education had a strong positive risk factor. Women with secondary or higher education were 1.85 times more likely to receive skilled care than uneducated women (AOR = 1.85; 95% CI 1.15–2.96; p = 0.01). Lumbini Province women had more than twice the odds of skilled delivery (AOR = 2.55; 95% CI 1.34–4.84; p = 0.004). Multiparous women were considerably less likely to utilize SBA (AOR = 0.52; 95% CI 0.37–0.73; p < 0.001). Four or more antenatal care (ANC) visits (AOR = 2.65; 95% CI 1.82–3.84; p < 0.001) and caesarean delivery (AOR = 6.75; 95% CI 3.35–13.62; p < 0.001) significantly associated the likelihood of skilled attendance. In urban settings, middle class (AOR = 1.86; 95% CI 1.28–2.70; p = 0.001) and rich (AOR = 1.93; 95% CI 1.28–2.93; p = 0.002) households had significantly higher odds of SBA utilization compared to poor households. Province-level inequalities persisted, with women from Gandaki Province having over three times higher odds of skilled attendance (AOR = 3.39; 95% CI 1.16–9.89; p = 0.03). Parity also showed a negative association (AOR = 0.45; 95% CI 0.30–0.67; p < 0.001). Women desiring more children were less likely to seek skilled delivery (AOR = 0.58; 95% CI 0.39–0.85; p = 0.005). As in rural areas, ≥ 4 ANC visits (AOR = 2.22; 95% CI 1.58–3.11; p < 0.001) and caesarean delivery (AOR = 3.21; 95% CI 1.83–5.65; p < 0.001) were the most significant associations [Table 3 ]. Table 3 Survey-weighted adjusted odds ratios for determinants of skilled birth attendance (SBA), stratified by rural and urban residence, Nepal NDHS 2022. Predictor Rural OR (95% CI) p Urban OR (95% CI) p Age 35 0.84 (0.40–1.76) 0.64 0.90 (0.33–2.44) 0.83 Education (woman) No education (ref) – – – – Basic 1.49 (0.99–2.25) 0.06 1.01 (0.65–1.56) 0.98 Secondary/Higher 1.85 (1.15–2.96) 0.01 1.23 (0.71–2.14) 0.45 Husband education No education (ref) – – – – Basic 0.69 (0.43–1.13) 0.14 1.46 (0.88–2.40) 0.14 Secondary/Higher 0.67 (0.40–1.13) 0.14 1.65 (0.97–2.80) 0.07 Don't know/Missing 0.76 (0.35–1.64) 0.48 1.25 (0.51–3.09) 0.62 Wealth Poor (ref) – – – – Middle 1.19 (0.80–1.79) 0.39 1.86 (1.28–2.70) 0.001 Rich 1.54 (0.87–2.71) 0.14 1.93 (1.28–2.93) 0.002 Province Province 1 (ref) – – – – Madhesh 1.14 (0.65–1.99) 0.65 1.21 (0.65–2.25) 0.55 Bagmati 1.23 (0.64–2.37) 0.53 0.87 (0.44–1.74) 0.70 Gandaki 1.40 (0.63–3.10) 0.41 3.39 (1.16–9.89) 0.03 Lumbini 2.55 (1.34–4.84) 0.004 1.60 (0.75–3.41) 0.22 Karnali 1.85 (0.95–3.59) 0.07 1.45 (0.78–2.68) 0.23 Sudurpashchim 1.60 (0.86–2.98) 0.14 1.24 (0.65–2.39) 0.51 Parity (2 + births) 0.52 (0.37–0.73) < 0.001 0.45 (0.30–0.67) < 0.001 1 birth (ref) – – – – Maternal age at first birth ≥ 20 1.33 (0.99–1.78) 0.06 1.14 (0.87–1.51) 0.33 < 20 (ref) – – – – ANC visits ≥ 4 2.65 (1.82–3.84) < 0.001 2.22 (1.58–3.11) < 0.001 < 4 visits (ref) – – – – Delivery mode (C-section) 6.75 (3.35–13.62) < 0.001 3.21 (1.83–5.65) < 0.001 Vaginal (ref) – – – – PNC by doctor 1.29 (0.94–1.77) 0.12 0.99 (0.71–1.40) 0.97 No doctor (ref) – – – – Fertility preference (want more) 0.82 (0.58–1.14) 0.24 0.58 (0.39–0.85) 0.005 No (ref) – – – – Working status 0.74 (0.52–1.06) 0.10 0.99 (0.70–1.40) 0.94 Not working (ref) – – – – Media exposure 0.96 (0.58–1.60) 0.88 0.83 (0.45–1.51) 0.54 No exposure (ref) – – – – Internet use (use regular) 1.28 (0.93–1.75) 0.12 1.36 (0.95–1.94) 0.10 No use (ref) – – – – Prenatal care attendance, 4 + ANC visits, caesarean delivery, and middle-to-rich wealth were positively associated with the outcome, whereas multiparity (2 or more births) was negatively associated, which shows the decreasing odds. In general, there was an overall outcome in socioeconomic and obstetric factors being the main determinants of the adjusted model [Figure 2 ]. The Area Under the Curve (AUC) = 0.7493, which also displays good results in terms of outcomes positive and outcome negative groups [Figure 3 ]. Discussion This paper investigated the prevalence, spatial distribution, and determinants of skilled birth attendance (SBA) of 2,785 ever-married women aged 15–49 years of age in Nepal based on 2022 nationally representative data of NDHS. Overall, 72.5% of births were attended by skilled, highlighting a substantial improvement compared with earlier national estimates but indicating that nearly one in four deliveries still occurred without professional assistance. The research results illustrate that there is a continued socioeconomic, geographic and obstetric inequality in SBA usage throughout the country. As anticipated by previous evidence, wealth status and attendance of the antenatal care (ANC) were noted to be excellent prognosticators of SBA. Women with middle and high-income families had higher likelihood to access skilled delivery care as they have higher financial capability to meet the cost of transport and healthcare related expenses. Equally, women with four or more ANC visits had over twice the odds of skilled birth attendance, highlighting the importance of consistent antenatal care. The findings correspond to Nepal, India, and Ethiopia that has constantly found household wealth, ANC utilization, and parity to be significant factors in skilled deliveries [ 18 , 23 – 27 ]. The odds of skilled attendance were lower in multiparous women as compared to their primiparous counterparts, a factor that indicates that prior successful birth experiences might lead to a perception that professional assistance is unnecessary [ 28 – 30 ]. On the contrary, the women who gave birth on or after 20 years were more likely to have a skilled care, which indicates their high maternal maturity, independence, or knowledge of risks of birth [ 31 ]. Caesarean birth was also a good predictor of skilled care indicating not only the association between obstetric complications and professional services but also the inequitable access to surgical care [ 32 ]. They’re existed Females in the Provinces of Gandaki, Lumbini and Karnal were more likely to experience skilled delivery, Madhesh and Sudurpashchim provinces indicated lower SBA coverage [ 22 ]. These results indicate imbalance with distribution of healthcare infrastructure, human resource and service provision, which supports the necessity of specific provincial intervention [ 33 , 34 ]. The stratified analyses also indicated the SBA use by rural women was more determined by factors such as education and health services as opposed to the socioeconomic factors in the urban area [ 35 ]. This difference underlines the contextual differences in determinants of maternal care, which are present in various settings, which can also be found in other LMIC studies [ 36 ]. There was a newfound relevance in behavioral and technological factors. Repeated use of the internet was also related to increased chances of skilled attendance and there is a likelihood that digital exposure can promote awareness of maternal health services [ 13 , 37 ]. Conversely, media exposure, employment status, and decision-making autonomy were not substantive predictors when other covariates were considered, which indicates that structural and health-service predictors could be important in influencing SBA utilization in Nepal than behavioral factors[ 34 , 38 – 41 ]. Policy Implications The priority in interventions to enhance SBA coverage should be laid on poorer household populations and disadvantaged populations, such as multiparous women, residents of lower-access provinces, etc. The effective plan that could boost the coverage of skilled delivery is the strengthening of ANC programs, developing provincial health facilities better, and using digital health platforms. These focused initiatives are necessary to hasten the achievement of the Sustainable Development Goal 3, to lower the maternal mortality and to deliver equal maternal health services throughout the country. The advantages of the research are that the data was nationally representative, the sampling design was rigorous, and a stepwise model selection method was applied to determine the most trustworthy predictors and adjust confounders. The weaknesses are that the information is cross-sectional and thus causal inferences cannot be made and the chances of biases in reporting related to self-reported experiences of deliveries. In spite of these shortcomings, the results offer an in-depth insight into the use of SBA and its predictors in modern Nepal. Conclusions This paper has discovered that skilled birth attendance in Nepal has improved but is still skewed. The ability of women in wealthier families, who made four or more ANC visits and those who gave birth through a caesarean section to get skilled attention during childbirth was higher. Nevertheless, female multiparous and women in poor provinces still had less SBA coverage. The disparities of rural and urban and provincial indicate that access to skilled maternal care is unequal. The strategies should aim at enhancing ANC use, limit financial and geographic constraints, and assist higher-parity mothers particularly in low-income areas. Intensifying these specific measures can contribute to achieving the national and global targets on the prevention of preventable maternal and newborn mortality in Nepal. Limitations To start with, the data were cross-sectional and thus inference of causality cannot be made. Second, there is a risk of self-reported information being affected by recall bias. Moreover, the dataset lacked certain important health-system variables that include the facility accessibility and the distance to services. The preliminary performance checks revealed that the end regression model exhibited a good level of discriminative performance (AUC = 0.74) but the Hosmer-Lemeshow test demonstrated that the final regression model had marginal calibration (p = 0.0437). This implies that the estimated probabilities might not be the most precise predicates of the actual results in all the risk groups. Declarations Data Availability Statement The data that support the findings of this study are publicly available from the Nepal Demographic and Health Survey (NDHS) 2022. These datasets can be accessed upon reasonable request through the DHS Program website: https://dhsprogram.com. Declaration of AI Assistance However, artificial intelligence-driven tools ChatGPT, Perplexity, and QuillBot were utilized only to enhance grammar, wording, and formatting during manuscript preparation. The authors carried out all the data analyses, interpretations as well as conclusions. CRediT Authorship Contribution Statement Md Salek Miah (First & Corresponding Author): Conceptualization, Methodology, Formal analysis, Supervision, Project administration, Writing – Original Draft. Mst. Labonna Begum & Fhameda Faija Lamia: Conceptualization, Investigation, Writing – Original Draft, Writing – Review & Editing, Supervision. Md. Yousuf Ali: Data curation, Resources, Writing – Original Draft. Nishat Anjume: Data curation, Writing – Review & Editing. Rima Akhter: Investigation, Writing – Review & Editing. Maria Bintey Kabir: Supervision, Writing – Review & Editing. Eshtiaq Ahamed: Data curation, Writing – Original Draft. Funding This research received no specific grant from any funding agency , commercial, or not-for-profit sectors. Consent for publication Not applicable. References Trends. in maternal mortality estimates 2000 to 2023. United Nations. Ensure healthy lives and promote well-being for all at all ages. Department of Economic and Social Affairs Sustainable Development. Unicef for every child. Maternal Mortality. Unicef for every child. 2025. Mugo NS, Agho KE, Dibley MJ. Risk Factors for Non-use of Skilled Birth Attendants: Analysis of South Sudan Household Survey, 2010. Matern Child Health J. 2016;20:1266–79. https://doi.org/10.1007/s10995-016-1928-x . Alkema L, Chou D, Hogan D, Zhang S, Moller AB, Gemmill A, et al. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: A systematic analysis by the un Maternal Mortality Estimation Inter-Agency Group. Lancet. 2016;387:462–74. https://doi.org/10.1016/S0140-6736(15)00838-7 . The state of. the world’s children: celebrating 20 years of the Convention on the Rights of the Child. Unicef; 2009. Zegeye B, Ahinkorah BO, Ameyaw EK, Budu E, Seidu AA, Olorunsaiye CZ, et al. Disparities in use of skilled birth attendants and neonatal mortality rate in Guinea over two decades. BMC Pregnancy Childbirth. 2022;22. https://doi.org/10.1186/s12884-021-04370-8 . Djoukou Olga Denise K, Mélèdje Marie-Dorothée K, Yacé Marie-Laurette A, Akoua T-K, Esme Marie Laure E, Williams Y, et al. Factors Associated with Maternal Health Service Utilization in Cote d’Ivoire: Analysis of the 2011 Ivorian Demographic and Health Survey. Sci J Public Health. 2019;7:115. https://doi.org/10.11648/j.sjph.20190704.12 . Wilunda C, Quaglio G, Putoto G, Takahashi R, Calia F, Abebe D, et al. Determinants of utilisation of antenatal care and skilled birth attendant at delivery in South West Shoa Zone, Ethiopia: A cross sectional study. Reprod Health. 2015;12. https://doi.org/10.1186/s12978-015-0067-y . Kwagala B, Nankinga O, Wandera SO, Ndugga P, Kabagenyi A. Empowerment, intimate partner violence and skilled birth attendance among women in rural Uganda. Reprod Health. 2016;13. https://doi.org/10.1186/s12978-016-0167-3 . Nepal. Demographic and Health Survey. 2016. NATIONAL FAMILY HEALTH SURVEY (NFHS. -4) 2015-16 INDIA. 2015. Celik Y, Hotchkiss DR. The socio-economic determinants of maternal health care utilization in Turkey. Soc Sci Med. 2000;50:1797–806. https://doi.org/10.1016/S0277-9536(99)00418-9 . Adewemimo AW, Msuya SE, Olaniyan CT, Adegoke AA. Utilisation of skilled birth attendance in Northern Nigeria: A cross-sectional survey. Midwifery. 2014;30. https://doi.org/10.1016/j.midw.2013.09.005 . Ayele GS, Melku AT, Belda SS. Utilization of skilled birth attendant at birth and associated factors among women who gave birth in the last 24 months preceding the survey in Gura Dhamole Woreda, Bale zone, southeast Ethiopia. BMC Public Health. 2019;19. https://doi.org/10.1186/s12889-019-7818-6 . Islam A, Nahar T, Siddiquee T, Toma AS, Hoque F, Hossain Z. Prevalence and determinants of utilizing skilled birth attendance during home delivery of pregnant women in India: Evidence from the Indian Demographic and Health Survey 2015–16. PLoS One. 2024;19 3 March. https://doi.org/10.1371/journal.pone.0295389 Thapa NR. Factors Associated with Skilled Birth Attendance in Nepal. Curriculum Dev J. 2024;32:125–41. https://doi.org/10.3126/cdj.v32i46.77010 . Thapa NR. Factors Associated with Skilled Birth Attendance in Nepal. Curriculum Dev J. 2024;32:125–41. https://doi.org/10.3126/cdj.v32i46.77010 . Nepal. Omer SB, Richards JL, Madhi SA, Tapia MD, Steinhoff MC, Aqil AR, et al. Three randomized trials of maternal influenza immunization in Mali, Nepal, and South Africa: Methods and expectations. Vaccine. 2015;33:3801–12. https://doi.org/10.1016/j.vaccine.2015.05.077 . Dhakal MS, Dhakal S, Van Teijlingen E, Amalraj Raja E, Dhakal KB. Skilled Care at Birth among Rural Women in Nepal: Practice and Challenges. J HEALTH POPUL NUTR. 2011;29:371–8. Shah R. Nepal Demographic and Health Survey 2022 Ministry of Health and Population New ERA Ministry of Health and Population. 2023. Abera M, Mariam AG, Belachew T. Predictors of Safe Delivery Service Mulumebet A. 95 PREDICTORS OF SAFE DELIVERY SERVICE UTILIZATION IN ARSI ZONE, SOUTH-EAST ETHIOPIA. Shimeka Teferra A, Mazengia Alemu F, Meseret Woldeyohannes S. Institutional delivery service utilization and associated factors among mothers who gave birth in the last 12 months in Sekela District, North West of Ethiopia: A community-based cross sectional study. 2012. Tessema ZT, Tesema GA. Pooled prevalence and determinants of skilled birth attendant delivery in East Africa countries: a multilevel analysis of Demographic and Health Surveys. Ital J Pediatr. 2020;46. https://doi.org/10.1186/s13052-020-00943-z . Jacobs C, Moshabela M, Maswenyeho S, Lambo N, Michelo C. Predictors of antenatal care, skilled birth attendance, and postnatal care utilization among the remote and poorest rural communities of Zambia: A multilevel analysis. Front Public Health. 2017;5:FEB. https://doi.org/10.3389/FPUBH.2017.00011 . Mati K, Adegoke KK, Michael-Asalu A, Salihu HM. Health insurance coverage and access to skilled birth attendance in Togo. Int J Gynecol Obstet. 2018;141:181–8. https://doi.org/10.1002/ijgo.12449 . Campbell OMR, Calvert C, Testa A, Strehlow M, Benova L, Keyes E, et al. The scale, scope, coverage, and capability of childbirth care. Lancet. 2016;388:2193–208. https://doi.org/10.1016/S0140-6736(16)31528-8 . Miller S, Abalos E, Chamillard M, Ciapponi A, Colaci D, Comandé D, et al. Beyond too little, too late and too much, too soon: a pathway towards evidence-based, respectful maternity care worldwide. Lancet. 2016;388:2176–92. https://doi.org/10.1016/S0140-6736(16)31472-6 . Tsegay Y, Gebrehiwot T, Goicolea I, Edin K, Lemma H, San Sebastian M. Determinants of antenatal and delivery care utilization in Tigray region, Ethiopia: a cross-sectional study. 2013. Campbell OMR, Graham WJ. Series Maternal Survival 2 Strategies for reducing maternal mortality: getting on with what works. . https://doi.org/10.1016/S0140 Seidu AA, Ahinkorah BO, Agbaglo E, Oduro JK, Amoah A, Yaya S. Factors associated with the utilisation of skilled delivery services in Papua New Guinea: Evidence from the 2016–2018 Demographic and Health Survey. Int Health. 2022;14:64–73. https://doi.org/10.1093/inthealth/ihab007 . Ahinkorah BO, Seidu AA, Agbaglo E, Adu C, Budu E, Hagan JE, et al. Determinants of antenatal care and skilled birth attendance services utilization among childbearing women in Guinea: evidence from the 2018 Guinea Demographic and Health Survey data. BMC Pregnancy Childbirth. 2021;21. https://doi.org/10.1186/s12884-020-03489-4 . Islam M, Yoshida S. MDG 5: How close are we to success? BJOG: An International Journal of Obstetrics and Gynaecology. 2009;116 SUPPL. 1:2–5. https://doi.org/10.1111/j.1471-0528.2009.02335.x Fekadu M, Regassa N. Skilled delivery care service utilization in Ethiopia: Analysis of rural-urban differentials based on national demographic and health survey (DHS) data. Afr Health Sci. 2014;14:974–84. https://doi.org/10.4314/ahs.v14i4.29 . Kibria GM, Al, Ghosh S, Hossen S, Barsha RAA, Sharmeen A, Uddin SMI. Factors affecting deliveries attended by skilled birth attendants in Bangladesh. Matern Health Neonatol Perinatol. 2017;3. https://doi.org/10.1186/s40748-017-0046-0 . Yakoob MY, Ali MA, Ali MU, Imdad A, Lawn JE, Van Den Broek N et al. The effect of providing skilled birth attendance and emergency obstetric care in preventing stillbirths. BMC Public Health. 2011;11 SUPPL. 3. https://doi.org/10.1186/1471-2458-11-S3-S7 Mannah MT, Warren C, Kuria S, Adegoke AA. Opportunities and challenges in implementing community based skilled birth attendance strategy in Kenya. BMC Pregnancy Childbirth. 2014;14. https://doi.org/10.1186/1471-2393-14-279 . Berhan Y, Berhan A, SKILLED HEALTH PERSONNEL ATTENDED DELIVERY AS A PROXY INDICATOR FOR MATERNAL, AND PERINATAL MORTALITY. : A SYSTEMATIC REVIEW. https://doi.org/10.4314/ejhs.v24i1.7S Jelka Z. Åhman Elisabeth. Neonatal and perinatal mortality: country, regional and global estimates 2004. World Health Organization; 2007. Alvarez JL, Gil R, Hernández V, Gil A. Factors associated with maternal mortality in Sub-Saharan Africa: An ecological study. BMC Public Health. 2009;9. https://doi.org/10.1186/1471-2458-9-462 . Additional Declarations No competing interests reported. Supplementary Files SupplymentaryFigureS1.pdf STROBEchecklistcrosssectional.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Editor invited by journal 05 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 30 Oct, 2025 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-7973803","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547524143,"identity":"ec236957-2050-4748-9b71-ea576d391aa6","order_by":0,"name":"Md Salek Miah","email":"data:image/png;base64,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","orcid":"","institution":"Shahjalal University of Science \u0026 Technology (SUST)","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Salek","lastName":"Miah","suffix":""},{"id":547524144,"identity":"4fe2e6d9-12dc-4987-9e01-e149f8ba43ff","order_by":1,"name":"Mst. Labonna Begum","email":"","orcid":"","institution":"Sylhet Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Mst.","middleName":"Labonna","lastName":"Begum","suffix":""},{"id":547524145,"identity":"91c7eee3-afe2-486c-a7da-3d08234e65c5","order_by":2,"name":"Fhameda Faija Lamia","email":"","orcid":"","institution":"Sylhet Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Fhameda","middleName":"Faija","lastName":"Lamia","suffix":""},{"id":547524146,"identity":"60ea1a77-f793-468c-bb57-055398bb3f14","order_by":3,"name":"Md. Yousuf Ali","email":"","orcid":"","institution":"University of Oulu","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Yousuf","lastName":"Ali","suffix":""},{"id":547524147,"identity":"b4c3c25b-845a-45b3-a6ee-3be83d0415a2","order_by":4,"name":"Nishat Anjume","email":"","orcid":"","institution":"Shahjalal University of Science \u0026 Technology (SUST)","correspondingAuthor":false,"prefix":"","firstName":"Nishat","middleName":"","lastName":"Anjume","suffix":""},{"id":547524148,"identity":"25f7be57-78b9-422f-98d0-094d6e2e7afe","order_by":5,"name":"Rima Akhter","email":"","orcid":"","institution":"Shahabuddin Medical College","correspondingAuthor":false,"prefix":"","firstName":"Rima","middleName":"","lastName":"Akhter","suffix":""},{"id":547524149,"identity":"79ba3abd-58b9-42c0-8f3d-76d0a3308010","order_by":6,"name":"Maria Bintey Kabir","email":"","orcid":"","institution":"Shaheed Suhrawardy Medical College","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Bintey","lastName":"Kabir","suffix":""},{"id":547524150,"identity":"b8bf1ee9-854c-4bc2-baa1-decd482711c0","order_by":7,"name":"Eshtiaq Ahamed","email":"","orcid":"","institution":"Shahjalal University of Science \u0026 Technology (SUST)","correspondingAuthor":false,"prefix":"","firstName":"Eshtiaq","middleName":"","lastName":"Ahamed","suffix":""}],"badges":[],"createdAt":"2025-10-28 21:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7973803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7973803/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96453638,"identity":"c194ead3-8236-4fe7-a5df-e71d42ffa405","added_by":"auto","created_at":"2025-11-21 10:01:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10573670,"visible":true,"origin":"","legend":"","description":"","filename":"SBANepalNDHS2022ManuscriptRevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/9c71844d75600cc54d8f3137.docx"},{"id":96380471,"identity":"afc96f85-a3e3-40f3-93ac-0db3990a5d0e","added_by":"auto","created_at":"2025-11-20 12:04:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10139,"visible":true,"origin":"","legend":"","description":"","filename":"6e7a63e98ffa47ceb099d0096b515d2c.json","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/5aa93f828e7471e794973df1.json"},{"id":96380470,"identity":"a578b1ea-ef59-4f6f-a130-80120dec7654","added_by":"auto","created_at":"2025-11-20 12:04:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33416,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcrosssectional.docx","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/e3cef8759a57d9382c4edf44.docx"},{"id":96380454,"identity":"9dc6af5a-32c5-4450-9618-446f86cfc74e","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21212,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/f28eca3daf9cfc07f5fe7756.pdf"},{"id":96380467,"identity":"0a0e6f6a-05ce-4d06-a4fd-09b4cdd1e7f9","added_by":"auto","created_at":"2025-11-20 12:04:12","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170168,"visible":true,"origin":"","legend":"","description":"","filename":"6e7a63e98ffa47ceb099d0096b515d2c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/6b9baf3b6bd253a7487036ac.xml"},{"id":96380459,"identity":"37e6d5aa-9aba-4033-9aae-2e28527ad0e1","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"eps","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":408,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage2.eps","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/d596331771ba4bee5f7da4d2.eps"},{"id":96453714,"identity":"7033c9fe-accd-48ae-bed7-2eb0c17e83ea","added_by":"auto","created_at":"2025-11-21 10:01:21","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3812792,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/6c2317da9ad9ed7635003e26.jpeg"},{"id":96380462,"identity":"c0077c9e-c84d-4ebc-9e40-ab3606c0d30e","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3812792,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/5a7184cd557660bc4fe605ea.jpeg"},{"id":96380468,"identity":"808946d2-aae9-434a-9186-d32ddb398eca","added_by":"auto","created_at":"2025-11-20 12:04:12","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2720776,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/950a6bd1dd95980ae8039069.jpeg"},{"id":96380460,"identity":"b8d4af08-99db-4b32-bb58-275d36f9254d","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22368,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/b66c7bae1a8b9c275b723de4.png"},{"id":96380457,"identity":"2259ce67-d8af-4481-b9e6-0d9af6a61347","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31364,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/bcc24d513044488bc2e11c3d.png"},{"id":96380465,"identity":"0979f2ec-b84e-4929-8d83-eace474dc9d0","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16783,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/2de9103e7a522cc610bff278.png"},{"id":96380466,"identity":"27b15f22-79dd-4e38-adc9-8db5515bf208","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163976,"visible":true,"origin":"","legend":"","description":"","filename":"6e7a63e98ffa47ceb099d0096b515d2c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/74171474c37fc5291af4e744.xml"},{"id":96453509,"identity":"d508bac7-9c4d-45ca-93e4-f61f1bf561f8","added_by":"auto","created_at":"2025-11-21 10:00:16","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":183197,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/a10a2d463395e45e0fc5b50f.html"},{"id":96453055,"identity":"9b004270-e4e2-4cd2-8222-bf352fb97e0b","added_by":"auto","created_at":"2025-11-21 09:57:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Skilled and Unskilled Births by Maternal Age, Religion, Education, Wealth, and Province in Nepal\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/21e54181367cfd70285dc402.png"},{"id":96453195,"identity":"30974610-2050-451b-a487-d3e1d0a14c67","added_by":"auto","created_at":"2025-11-21 09:58:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of survey-weighted adjusted odds ratios for skilled birth attendance in Nepal\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/41857074953eb5aa4b32acb9.png"},{"id":96380453,"identity":"8ed77f56-7800-4211-912e-06213c8aedd2","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvey-weighted ROC curve for predicting skilled birth attendance in Nepal\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/225d2fa6a74ea5fae96dd832.png"},{"id":96456782,"identity":"e331a607-c8bb-42e0-96e0-fdd77a94a838","added_by":"auto","created_at":"2025-11-21 10:07:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2222969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/ebb44153-d04f-4abc-9cc4-9208ad75cfb1.pdf"},{"id":96380452,"identity":"ba8e3467-5dbd-46de-9ece-d30012bd61b2","added_by":"auto","created_at":"2025-11-20 12:04:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21212,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/68cce4c3f14ae4edfd5fae1d.pdf"},{"id":96453362,"identity":"9b7f7cfc-8d77-41d4-bf8c-071d096cb6de","added_by":"auto","created_at":"2025-11-21 09:59:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33416,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcrosssectional.docx","url":"https://assets-eu.researchsquare.com/files/rs-7973803/v1/7bc6f7d7875e089df92a3ec6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants and Spatial Variation of Skilled Birth Assistance in Nepal: Insights from NDHS 2022","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThere have been significant changes in maternal health in the past decades, maternal mortality continues to be a significant issue in the population, especially in low- and middle-income nations (LMICs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Sustainable Development Goal (SDG) 3 seeks to lower the maternal mortality rate (MMR) to less than 70 deaths per 100,000 live births across the world by 2030 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. But the development towards this purpose has been unequal[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. LMICs continue to experience more than 90 percent of maternal mortality, and South Asia alone constitutes almost a fifth [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Majority of these deaths are avoidable and occur as a result of complications during pregnancy, labor or a few days after birth[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is thus a vital requirement that maternal health services be available to address preventable maternal deaths[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Skilled Birth Attendance (SBA) is one of the most effective methods of reducing both maternal and neonatal morbidity and mortality as Skilled Birth Attended (SBA) has become one of the essential maternal health services [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Well trained attendants will be able to diagnose and treat obstetric complications, give timely referrals and safer delivery among mothers and babies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nevertheless, there are still inequalities in SBA coverage both across as well as within countries even with robust policy commitments and investment in the health system, especially in the rural and disadvantaged areas [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The extent of the use of SBA in South Asia is quite different among the countries. In 2016, 58 percent of births in Nepal were served by skilled personnel[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], 81 percent in India[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As has been proven by evidence provided in LMICs, the usage of SBA is predetermined by a complex of individual, house, and situational factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Research in other nations like Guinea, Kenya, and Tanzania have revealed that maternal education, household wealth, parity, antenatal care (ANC) visits, and the closeness to health facilities are the main predictors of SBA use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Likewise, a study in India and Ethiopia has determined that place of residence, wealth, and media exposure have a significant impact on the uptake of services [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Past research has also pointed out parity, maternal education, preparedness to birth, and distance to health facilities in Nepal as key determinants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Nevertheless, a majority of such studies had been restricted either to a particular province or to narrow sample sizes and little represented the country wide spatial and rural-urban differences [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although the country is better covered nationally, there is a lack of evidence regarding spatial distribution and rural-urban differences in SBA utilization in Nepal regarding recent nationally representative data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, past analyses have tended to have incomplete model selection processes that could help to determine the most powerful predictors of skilled delivery care [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These gaps need to be filled in so as to have a better grasp of the women and areas left behind. Thus, the following research questions are addressed by the study: (1) to determine the prevalence and spatial variation of skilled birth attendance among the ever-married women aged 15\u0026ndash;49 years in Nepal; (2) the identification of individual- and community-level predictors of SBA through a stepwise model selection strategy to provide the methodological strength of the study; and (3) investigate the rural to urban differences in the predictors. The results will inspire policymakers and general health practitioners to develop more specific, balanced approaches to alleviate skilled delivery coverage and to enhance the development of Nepal that is focused towards meeting SDG 3 by 2030.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eThis study used secondary data from the 2022 Nepal Demographic and Health Survey (NDHS), cross-sectional study conducted by the Ministry of Health and Population (MoHP) between January 5 and June 22, 2022, in collaboration with New ERA and ICF under the global DHS Program. Nepal is a landlocked South Asian country bordered by China and India and is administratively divided into seven provinces and 77 districts across three ecological zones: Mountain, Hill, and Terai. The NDHS 2022 employed a two-stage stratified cluster sampling design to ensure national representativeness. In the initial stage, 383 enumeration areas (112 urban and 271 rural) were selected with probability proportional to size based on the 2021 National Population and Housing Census. In the last stage, 30 households were systematically selected from each EA using updated household listings [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population and Sample\u003c/h3\u003e\n\u003cp\u003eThe study population included ever-married women aged 15\u0026ndash;49 years who had given birth within five years preceding the survey. After applying inclusion criteria and sampling weights, a total of \u003cb\u003e2,785 women\u003c/b\u003e were analyzed [\u003cb\u003esee in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e]..\u003c/p\u003e\n\u003ch3\u003eOutcome variable\u003c/h3\u003e\n\u003cp\u003eThe outcome variable was \u003cb\u003eskilled birth attendance (SBA)\u003c/b\u003e, defined as delivery assisted by a qualified health professional such as a doctor, nurse, or midwife. Deliveries attended by these providers were coded as \u0026ldquo;1\u0026thinsp;=\u0026thinsp;skilled,\u0026rdquo; while deliveries attended by traditional birth attendants, relatives, or others were coded as \u0026ldquo;0\u0026thinsp;=\u0026thinsp;unskilled.\u0026rdquo;\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIndependent variables included maternal age, education, husband\u0026rsquo;s education, household wealth, parity, ANC visits, employment status, media status, decision-making autonomy, place of residence, province, and province.\u003c/p\u003e\n\u003ch3\u003eData Management\u003c/h3\u003e\n\u003cp\u003eData were cleaned, recoded, and verified against the NDHS 2022 codebook. Sampling weights (v005), primary sampling units (v021), and stratification variables (v023) were applied by the \u003cem\u003esvyset\u003c/em\u003e command in STATA 17 to account for the complex survey design, and R studio 4.5.1 used for spatial analysis. This study maintains STROBE guidelines.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis and Model Building\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarize participants\u0026rsquo; characteristics and SBA prevalence. Bivariate associations were assessed using chi-square tests. Multivariable logistic regression was conducted using a \u003cb\u003estepwise selection approach\u003c/b\u003e to detect risk factors of SBA utilization. Eighteen sequential models were fitted, and the \u003cb\u003e16th model\u003c/b\u003e was selected as the final model based on \u003cb\u003elowest AIC (2877.235) and BIC (3031.468)\u003c/b\u003e values. This model included key confounders, including maternal age, education, husband\u0026rsquo;s education, household wealth, parity, ANC visits, employment status, media exposure, and residence-related factors. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eModel diagnostics\u003c/h3\u003e\n\u003cp\u003eFor checking multicollinearity using variance inflation factors (VIF\u0026thinsp;=\u0026thinsp;1.87) assessing model calibration with the Hosmer Lemeshow goodness-of-fit test, and evaluating discriminative ability used Receiver Operating Characteristic curve (AUCROC).\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThe NDHS 2022 received ethical approval from the \u003cb\u003eNepal Health Research Council (NHRC)\u003c/b\u003e and the \u003cb\u003eICF Institutional Review Board\u003c/b\u003e. Written informed consent was obtained from all participants prior to interviews. Permission to use the anonymized dataset for secondary analysis was granted by the \u003cb\u003eDHS Program\u003c/b\u003e, and details available at The DHS Program - Protecting the Privacy of DHS Survey Respondents.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMost deliveries occurred among women aged 20\u0026ndash;34 years (\u0026asymp;\u0026thinsp;83%), with slightly higher unskilled births among women\u0026thinsp;\u0026gt;\u0026thinsp;35 years. Skilled births were more common among Hindu women (\u0026asymp;\u0026thinsp;86%) and those with at least basic education and from middle-to-rich households, whereas unskilled deliveries predominated among poorer, less-educated women. Regions with lower prevalence of skilled births were seen in Madhesh and Sudurpashchim provinces, which points to unfair distributions in maternal care access in the regions [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll in all, trained professionals attended to 72.5% of the births, and the unprofessional attendants took care of 27.5% of them. The age of the mother slightly related to skilled birth assistance (p\u0026thinsp;=\u0026thinsp;0.056). Women who received skilled care was more among the 20\u0026ndash;34 years (60.14%) than the \u0026lt;\u0026thinsp;20 years (7.72%) and the over 35 years (4.63%). Religion had marginal significant difference (p\u0026thinsp;=\u0026thinsp;0.059) whereby, Hindu mothers were more likely to utilize skilled birth services as compared to non-Hindu women. Maternal age at first birth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age at first sex (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significant. Other significant factors were parity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), number of children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); primiparous mothers and single-child mothers had a higher possibility of seeking skilled care. There was a significant association with frequent antenatal care (4 visits and above, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and caesarean delivery (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). An evident wealth gradient was presented (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): 22.98% of women in the rich population had been presented with skilled help in contrast to 35.66% of the poor. Skilled attendance was significantly associated with the living in urban residence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and possessing health insurance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The employment status (p\u0026thinsp;=\u0026thinsp;0.006) also determined utilization, working women had better skilled attendance (48.04%) when compared to non-working women (24.45%). The education level of both husband and woman was a significant risk factor (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); the rate of skilled assistance rose as the level of education improved. Females who were exposed to mass media (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those that used internet regularly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were considerably more likely to receive skilled assistance. The inequality across the regions was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The largest percentages of skilled birth attendance were reported in Bagmati and Gandaki provinces and the lowest percentage was reported in Karnali and Sudurpashchim [Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of skilled and unskilled births by demographic, socioeconomic, obstetric, and contextual variables in Nepal (NDHS 2022)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSkilled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eUnskilled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c9\" namest=\"c8\" rowspan=\"2\"\u003e\u003cp\u003eTotal n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSkilled Birth Assistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2,019 (72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e766 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographic Variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal Age\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e215 (7.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66 (2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e281(10.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e20\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1,675 (60.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e635 (22.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e2,310(82.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e129 (4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65 (2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e194(6.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligious Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eHindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1,734 (62.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e636(83.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e2,370(85.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNon-Hindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e285 (10.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e130(4.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e415(14.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e745(26.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e297(10.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1,042(37.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1,274(45.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e469(16.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1,743(62.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal age at first birth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e884(31.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e432(15.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1,316(47.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1,135(40.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e334(11.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1,469(52.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at first sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e831(29.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e428(55.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1,259(45.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1,188(42.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e338(12.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1,526(54.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold \u0026amp; Socioeconomic Variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e993 (35.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e532(19.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,525(54.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e386 (13.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e120(4.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e506(18.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e640 (22.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e114(4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e754 (27.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eresidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1,073(38.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e346(13.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,419(50.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e946(33.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e420(15.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,366(49.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5 member\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1,066(38.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e381(49.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,447(51.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5 member\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e953(34.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e385(13.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,338(48.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Insurance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1,789(64.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e720(25.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,509(90.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e230(8.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e46(1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e276(9.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorking Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNot working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e681(24.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e217(7.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e898(32.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1,338(48.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e549(19.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,887(67.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHusband Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e136(4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e110(3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e246(8.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBasic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e747(26.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e348(12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,095(39.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1,096(39.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e287(10.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,383(49.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWoman Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e290(10.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e232(8.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e522(18.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBasic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e1,299(46.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e467(16.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,766(63.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e430(15.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e67(2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e497(17.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eObstetric \u0026amp; Reproductive Variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimiparo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e923(33.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e194(6.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,117(40.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiparo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,096(39.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e572(20.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,668(59.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003echild_cat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e959(34.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e218(7.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,177(42.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple Child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,060(38.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e548(19.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,608(57.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANC visit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e238(8.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e243(8.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e481(17.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,781(63.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e523(18.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,304(82.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDelivery mode\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaginal delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,610(57.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e735(26.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,345(84.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaesarean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e409(14.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e31(1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e440(15.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall / U\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e315(11.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e128(4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e443(15.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,704(61.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e638(22.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,342(84.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePNC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo prenatal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e977(35.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e476(17.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,453(52.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrenatal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,042(37.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e290(10.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,332(47.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePressure to pregnant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot pressured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,863(67.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e690(24.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,553(92.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePressured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e142(5.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e72(2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e214(7.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSexual Activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot active\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e911(32.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e333(11.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,244(44.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,105(39.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e431(15.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,536(55.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003efert_pref\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo not Want\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,220(43.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e493(17.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,713(61.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWant More\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e799(28.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e273(9.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,072(38.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003egenital_ulcer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.124\u003c/p\u003e\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,936(69.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e744(26.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,680(96.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e80(2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e21(0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101(3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia Exposure \u0026amp; Empowerment Variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMass Media\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo media\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e87(3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e74(2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e161(5.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny media\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,932(69.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e692(24.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,624(94.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInternet Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e929(33.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e515(18.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,444(51.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse regular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,090(39.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e251(9.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,341(48.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDecision making\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.539\u003c/p\u003e\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,272(45.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e493(17.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,765(63.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e733(26.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e269(9.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,002(36.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIPV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1,639(59.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e609(21.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,248(80.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e374(13.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e154(5.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e528(19.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGeographic \u0026amp; Contextual Variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvince\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKoshi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e298 (10.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e126 (4.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e424 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMadhesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e375(13.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e185(6.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e560(20.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBagmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e243(8.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e89(3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e332(11.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGandaki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e177(6.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e39(1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e216(7.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbini\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e319(11.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e70(2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e389(13.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarnali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e317(11.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e136(4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e453(16.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSudurpash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e290(10.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e121(4.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e411(14.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMaternal age was not associated with SBA. Women aged 20\u0026ndash;34 years (AOR\u0026thinsp;=\u0026thinsp;0.82; 95% CI: 0.53\u0026ndash;1.28) and \u0026gt;\u0026thinsp;35 years (AOR\u0026thinsp;=\u0026thinsp;0.83; 95% CI: 0.43\u0026ndash;1.61) had comparable probabilities of skilled attendance with women under 20 years old. The educational level of women had a marginal significant positive association: secondary or higher-educated women had 1.43 times higher odds for skilled help compared to illiterate women (AOR\u0026thinsp;=\u0026thinsp;1.43; 95% CI: 0.99\u0026ndash;2.06; p\u0026thinsp;=\u0026thinsp;0.054). Husband's level of education contributed minimally to SBA use.\u003c/p\u003e\u003cp\u003eHousehold income was a significant determinant. Compared with poor households, middle-income (AOR\u0026thinsp;=\u0026thinsp;1.60; 95% CI: 1.22\u0026ndash;2.11; p\u0026thinsp;=\u0026thinsp;0.001) and affluent households (AOR\u0026thinsp;=\u0026thinsp;1.73; 95% CI: 1.25\u0026ndash;2.40; p\u0026thinsp;=\u0026thinsp;0.001) women were more likely to deliver with skilled delivery care. Residence location was nonsignificant (p\u0026thinsp;=\u0026thinsp;0.523).\u003c/p\u003e\u003cp\u003eThere were significant provincial variations. Women in Gandaki (AOR\u0026thinsp;=\u0026thinsp;2.09; 95% CI: 1.15\u0026ndash;3.79; p\u0026thinsp;=\u0026thinsp;0.015), Lumbini (AOR\u0026thinsp;=\u0026thinsp;1.99; 95% CI: 1.20\u0026ndash;3.31; p\u0026thinsp;=\u0026thinsp;0.008), and Karnali (AOR\u0026thinsp;=\u0026thinsp;1.60; 95% CI: 1.01\u0026ndash;2.54; p\u0026thinsp;=\u0026thinsp;0.045) provinces had higher odds of skilled attendance compared to women in Province 1, and no variation was observed in other provinces.\u003c/p\u003e\u003cp\u003eParity was negatively associated with SBA use (AOR\u0026thinsp;=\u0026thinsp;0.47; 95% CI: 0.36\u0026ndash;0.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that multiparous women were less likely to have skilled attendance compared to primiparous women. Women who had a first birth at \u0026ge;\u0026thinsp;20 years were more likely to have skilled care (AOR\u0026thinsp;=\u0026thinsp;1.23; 95% CI: 1.00\u0026ndash;1.50; p\u0026thinsp;=\u0026thinsp;0.047). ANC visit frequency was among the strongest predictors: women who had four or more ANC visits were more than twice as likely to have skilled birth care (AOR\u0026thinsp;=\u0026thinsp;2.30; 95% CI: 1.80\u0026ndash;2.94; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Delivery mode was highly significant as caesarean sections were nearly three times higher skilled birth assistance (AOR\u0026thinsp;=\u0026thinsp;3.86; 95% CI: 2.42\u0026ndash;6.16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThose women in the need for additional children had significantly reduced odds of receiving skilled care (AOR\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.50\u0026ndash;0.86; p\u0026thinsp;=\u0026thinsp;0.002. But frequent use of the internet was also significant as frequently internet use, women used internet showed a 1.32 times increased probability of getting skilled care (AOR\u0026thinsp;=\u0026thinsp;1.32; 95% CI: 1.03\u0026ndash;1.69; p\u0026thinsp;=\u0026thinsp;0.027) [Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSurvey-weighted adjusted odds ratios for determinants of skilled birth attendance (SBA) in Nepal.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\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;20 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.53\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.43\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation (Woman)\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\u003eNone (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (0.87\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary/Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43 (0.99\u0026ndash;2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHusband\u0026rsquo;s Education\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\u003eNone (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12 (0.78\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary/Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 (0.81\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon\u0026rsquo;t know/Missing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06 (0.55\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth\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\u003ePoor (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60 (1.22\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73 (1.25\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\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 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.72\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvince\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\u003eProvince1 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMadhesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19 (0.77\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBagmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.60\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGandaki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.09 (1.15\u0026ndash;3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbini\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.99 (1.20\u0026ndash;3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarnali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60 (1.01\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSudurpashchim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37 (0.87\u0026ndash;2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity\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\u003ePrimiparous (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiparous (2\u0026thinsp;+\u0026thinsp;births)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47 (0.36\u0026ndash;0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal Age at First Birth\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;20 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23 (1.00\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANC 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;4 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30 (1.80\u0026ndash;2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDelivery Mode\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\u003eVaginal (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaesarean section\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.86 (2.42\u0026ndash;6.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePNC Doctor\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\u003eNone (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrenatal doctor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10 (0.87\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFertility Preference\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\u003eWant none (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWant more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.66 (0.50\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorking 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\u003eNot working (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.91 (0.70\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia Exposure\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\u003eNone (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.57\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInternet Use\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\u003eNone (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse regular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32 (1.03\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAge, Education (Woman), Husband\u0026rsquo;s Education, Wealth, Residence, Province, Parity, Maternal Age at First Birth, ANC Visits, Delivery Mode, PNC Doctor, Fertility Preference, Working Status, Media Exposure, Internet Use\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn rural women, education had a strong positive risk factor. Women with secondary or higher education were 1.85 times more likely to receive skilled care than uneducated women (AOR\u0026thinsp;=\u0026thinsp;1.85; 95% CI 1.15\u0026ndash;2.96; p\u0026thinsp;=\u0026thinsp;0.01). Lumbini Province women had more than twice the odds of skilled delivery (AOR\u0026thinsp;=\u0026thinsp;2.55; 95% CI 1.34\u0026ndash;4.84; p\u0026thinsp;=\u0026thinsp;0.004). Multiparous women were considerably less likely to utilize SBA (AOR\u0026thinsp;=\u0026thinsp;0.52; 95% CI 0.37\u0026ndash;0.73; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFour or more antenatal care (ANC) visits (AOR\u0026thinsp;=\u0026thinsp;2.65; 95% CI 1.82\u0026ndash;3.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and caesarean delivery (AOR\u0026thinsp;=\u0026thinsp;6.75; 95% CI 3.35\u0026ndash;13.62; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly associated the likelihood of skilled attendance.\u003c/p\u003e\u003cp\u003eIn urban settings, middle class (AOR\u0026thinsp;=\u0026thinsp;1.86; 95% CI 1.28\u0026ndash;2.70; p\u0026thinsp;=\u0026thinsp;0.001) and rich (AOR\u0026thinsp;=\u0026thinsp;1.93; 95% CI 1.28\u0026ndash;2.93; p\u0026thinsp;=\u0026thinsp;0.002) households had significantly higher odds of SBA utilization compared to poor households. Province-level inequalities persisted, with women from Gandaki Province having over three times higher odds of skilled attendance (AOR\u0026thinsp;=\u0026thinsp;3.39; 95% CI 1.16\u0026ndash;9.89; p\u0026thinsp;=\u0026thinsp;0.03). Parity also showed a negative association (AOR\u0026thinsp;=\u0026thinsp;0.45; 95% CI 0.30\u0026ndash;0.67; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women desiring more children were less likely to seek skilled delivery (AOR\u0026thinsp;=\u0026thinsp;0.58; 95% CI 0.39\u0026ndash;0.85; p\u0026thinsp;=\u0026thinsp;0.005). As in rural areas, \u0026ge;\u0026thinsp;4 ANC visits (AOR\u0026thinsp;=\u0026thinsp;2.22; 95% CI 1.58\u0026ndash;3.11; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and caesarean delivery (AOR\u0026thinsp;=\u0026thinsp;3.21; 95% CI 1.83\u0026ndash;5.65; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the most significant associations [Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e].\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\u003eSurvey-weighted adjusted odds ratios for determinants of skilled birth attendance (SBA), stratified by rural and urban residence, Nepal NDHS 2022.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUrban OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80 (0.46\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.46\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.84 (0.40\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.33\u0026ndash;2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation (woman)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49 (0.99\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.65\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary/Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85 (1.15\u0026ndash;2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23 (0.71\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHusband education\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.69 (0.43\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46 (0.88\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary/Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 (0.40\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65 (0.97\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDon't know/Missing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76 (0.35\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25 (0.51\u0026ndash;3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWealth\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19 (0.80\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86 (1.28\u0026ndash;2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54 (0.87\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93 (1.28\u0026ndash;2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvince\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince 1 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMadhesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14 (0.65\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.65\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBagmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23 (0.64\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.44\u0026ndash;1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGandaki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 (0.63\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.39 (1.16\u0026ndash;9.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbini\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.55 (1.34\u0026ndash;4.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.60 (0.75\u0026ndash;3.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarnali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85 (0.95\u0026ndash;3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45 (0.78\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSudurpashchim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60 (0.86\u0026ndash;2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24 (0.65\u0026ndash;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity (2\u0026thinsp;+\u0026thinsp;births)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.52 (0.37\u0026ndash;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45 (0.30\u0026ndash;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 birth (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal age at first birth\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33 (0.99\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14 (0.87\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANC visits\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.65 (1.82\u0026ndash;3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.22 (1.58\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4 visits (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDelivery mode (C-section)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.75 (3.35\u0026ndash;13.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.21 (1.83\u0026ndash;5.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaginal (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePNC by doctor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.29 (0.94\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.71\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo doctor (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFertility preference (want more)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.58\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58 (0.39\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorking status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74 (0.52\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.70\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot working (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedia exposure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.58\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 (0.45\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo exposure (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInternet use (use regular)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28 (0.93\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36 (0.95\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo use (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\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\u003ePrenatal care attendance, 4\u0026thinsp;+\u0026thinsp;ANC visits, caesarean delivery, and middle-to-rich wealth were positively associated with the outcome, whereas multiparity (2 or more births) was negatively associated, which shows the decreasing odds. In general, there was an overall outcome in socioeconomic and obstetric factors being the main determinants of the adjusted model [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Area Under the Curve (AUC)\u0026thinsp;=\u0026thinsp;0.7493, which also displays good results in terms of outcomes positive and outcome negative groups [Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper investigated the prevalence, spatial distribution, and determinants of skilled birth attendance (SBA) of 2,785 ever-married women aged 15\u0026ndash;49 years of age in Nepal based on 2022 nationally representative data of NDHS. Overall, 72.5% of births were attended by skilled, highlighting a substantial improvement compared with earlier national estimates but indicating that nearly one in four deliveries still occurred without professional assistance.\u003c/p\u003e\u003cp\u003eThe research results illustrate that there is a continued socioeconomic, geographic and obstetric inequality in SBA usage throughout the country. As anticipated by previous evidence, wealth status and attendance of the antenatal care (ANC) were noted to be excellent prognosticators of SBA. Women with middle and high-income families had higher likelihood to access skilled delivery care as they have higher financial capability to meet the cost of transport and healthcare related expenses. Equally, women with four or more ANC visits had over twice the odds of skilled birth attendance, highlighting the importance of consistent antenatal care.\u003c/p\u003e\u003cp\u003eThe findings correspond to Nepal, India, and Ethiopia that has constantly found household wealth, ANC utilization, and parity to be significant factors in skilled deliveries [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The odds of skilled attendance were lower in multiparous women as compared to their primiparous counterparts, a factor that indicates that prior successful birth experiences might lead to a perception that professional assistance is unnecessary [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. On the contrary, the women who gave birth on or after 20 years were more likely to have a skilled care, which indicates their high maternal maturity, independence, or knowledge of risks of birth [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Caesarean birth was also a good predictor of skilled care indicating not only the association between obstetric complications and professional services but also the inequitable access to surgical care [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. They\u0026rsquo;re existed Females in the Provinces of Gandaki, Lumbini and Karnal were more likely to experience skilled delivery, Madhesh and Sudurpashchim provinces indicated lower SBA coverage [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These results indicate imbalance with distribution of healthcare infrastructure, human resource and service provision, which supports the necessity of specific provincial intervention [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The stratified analyses also indicated the SBA use by rural women was more determined by factors such as education and health services as opposed to the socioeconomic factors in the urban area [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis difference underlines the contextual differences in determinants of maternal care, which are present in various settings, which can also be found in other LMIC studies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. There was a newfound relevance in behavioral and technological factors. Repeated use of the internet was also related to increased chances of skilled attendance and there is a likelihood that digital exposure can promote awareness of maternal health services [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Conversely, media exposure, employment status, and decision-making autonomy were not substantive predictors when other covariates were considered, which indicates that structural and health-service predictors could be important in influencing SBA utilization in Nepal than behavioral factors[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePolicy Implications\u003c/h2\u003e\u003cp\u003eThe priority in interventions to enhance SBA coverage should be laid on poorer household populations and disadvantaged populations, such as multiparous women, residents of lower-access provinces, etc. The effective plan that could boost the coverage of skilled delivery is the strengthening of ANC programs, developing provincial health facilities better, and using digital health platforms. These focused initiatives are necessary to hasten the achievement of the Sustainable Development Goal 3, to lower the maternal mortality and to deliver equal maternal health services throughout the country. The advantages of the research are that the data was nationally representative, the sampling design was rigorous, and a stepwise model selection method was applied to determine the most trustworthy predictors and adjust confounders.\u003c/p\u003e\u003cp\u003eThe weaknesses are that the information is cross-sectional and thus causal inferences cannot be made and the chances of biases in reporting related to self-reported experiences of deliveries. In spite of these shortcomings, the results offer an in-depth insight into the use of SBA and its predictors in modern Nepal.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis paper has discovered that skilled birth attendance in Nepal has improved but is still skewed. The ability of women in wealthier families, who made four or more ANC visits and those who gave birth through a caesarean section to get skilled attention during childbirth was higher. Nevertheless, female multiparous and women in poor provinces still had less SBA coverage. The disparities of rural and urban and provincial indicate that access to skilled maternal care is unequal. The strategies should aim at enhancing ANC use, limit financial and geographic constraints, and assist higher-parity mothers particularly in low-income areas. Intensifying these specific measures can contribute to achieving the national and global targets on the prevention of preventable maternal and newborn mortality in Nepal.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eTo start with, the data were cross-sectional and thus inference of causality cannot be made. Second, there is a risk of self-reported information being affected by recall bias. Moreover, the dataset lacked certain important health-system variables that include the facility accessibility and the distance to services. The preliminary performance checks revealed that the end regression model exhibited a good level of discriminative performance (AUC = 0.74) but the Hosmer-Lemeshow test demonstrated that the final regression model had marginal calibration (p = 0.0437). This implies that the estimated probabilities might not be the most precise predicates of the actual results in all the risk groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are publicly available from the Nepal Demographic and Health Survey (NDHS) 2022. These datasets can be accessed upon reasonable request through the DHS Program website: https://dhsprogram.com.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI Assistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHowever, artificial intelligence-driven tools ChatGPT, Perplexity, and QuillBot were utilized only to enhance grammar, wording, and formatting during manuscript preparation. The authors carried out all the data analyses, interpretations as well as conclusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Authorship Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Salek Miah (First \u0026amp; Corresponding Author):\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Supervision, Project administration, Writing – Original Draft. \u003cstrong\u003eMst. Labonna Begum \u0026amp; Fhameda Faija Lamia:\u003c/strong\u003e Conceptualization, Investigation, Writing – Original Draft, Writing – Review \u0026amp; Editing, Supervision. \u003cstrong\u003eMd. Yousuf Ali:\u003c/strong\u003e Data curation, Resources, Writing – Original Draft. \u003cstrong\u003eNishat Anjume:\u003c/strong\u003e Data curation, Writing – Review \u0026amp; Editing. \u003cstrong\u003eRima Akhter:\u003c/strong\u003e Investigation, Writing – Review \u0026amp; Editing. \u003cstrong\u003eMaria Bintey Kabir:\u003c/strong\u003e Supervision, Writing – Review \u0026amp; Editing. \u003cstrong\u003eEshtiaq Ahamed:\u003c/strong\u003e Data curation, Writing – Original Draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received \u003cstrong\u003eno specific grant from any funding agency\u003c/strong\u003e, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrends. in maternal mortality estimates 2000 to 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations. Ensure healthy lives and promote well-being for all at all ages. Department of Economic and Social Affairs Sustainable Development.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnicef for every child. Maternal Mortality. Unicef for every child. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMugo NS, Agho KE, Dibley MJ. Risk Factors for Non-use of Skilled Birth Attendants: Analysis of South Sudan Household Survey, 2010. Matern Child Health J. 2016;20:1266\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10995-016-1928-x\u003c/span\u003e\u003cspan address=\"10.1007/s10995-016-1928-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkema L, Chou D, Hogan D, Zhang S, Moller AB, Gemmill A, et al. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: A systematic analysis by the un Maternal Mortality Estimation Inter-Agency Group. Lancet. 2016;387:462\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(15)00838-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)00838-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe state of. the world\u0026rsquo;s children: celebrating 20 years of the Convention on the Rights of the Child. Unicef; 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZegeye B, Ahinkorah BO, Ameyaw EK, Budu E, Seidu AA, Olorunsaiye CZ, et al. Disparities in use of skilled birth attendants and neonatal mortality rate in Guinea over two decades. BMC Pregnancy Childbirth. 2022;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-021-04370-8\u003c/span\u003e\u003cspan address=\"10.1186/s12884-021-04370-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDjoukou Olga Denise K, M\u0026eacute;l\u0026egrave;dje Marie-Doroth\u0026eacute;e K, Yac\u0026eacute; Marie-Laurette A, Akoua T-K, Esme Marie Laure E, Williams Y, et al. Factors Associated with Maternal Health Service Utilization in Cote d\u0026rsquo;Ivoire: Analysis of the 2011 Ivorian Demographic and Health Survey. Sci J Public Health. 2019;7:115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11648/j.sjph.20190704.12\u003c/span\u003e\u003cspan address=\"10.11648/j.sjph.20190704.12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilunda C, Quaglio G, Putoto G, Takahashi R, Calia F, Abebe D, et al. Determinants of utilisation of antenatal care and skilled birth attendant at delivery in South West Shoa Zone, Ethiopia: A cross sectional study. Reprod Health. 2015;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12978-015-0067-y\u003c/span\u003e\u003cspan address=\"10.1186/s12978-015-0067-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwagala B, Nankinga O, Wandera SO, Ndugga P, Kabagenyi A. Empowerment, intimate partner violence and skilled birth attendance among women in rural Uganda. Reprod Health. 2016;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12978-016-0167-3\u003c/span\u003e\u003cspan address=\"10.1186/s12978-016-0167-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNepal. Demographic and Health Survey. 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNATIONAL FAMILY HEALTH SURVEY (NFHS. -4) 2015-16 INDIA. 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCelik Y, Hotchkiss DR. The socio-economic determinants of maternal health care utilization in Turkey. Soc Sci Med. 2000;50:1797\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0277-9536(99)00418-9\u003c/span\u003e\u003cspan address=\"10.1016/S0277-9536(99)00418-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdewemimo AW, Msuya SE, Olaniyan CT, Adegoke AA. Utilisation of skilled birth attendance in Northern Nigeria: A cross-sectional survey. Midwifery. 2014;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.midw.2013.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.midw.2013.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyele GS, Melku AT, Belda SS. Utilization of skilled birth attendant at birth and associated factors among women who gave birth in the last 24 months preceding the survey in Gura Dhamole Woreda, Bale zone, southeast Ethiopia. BMC Public Health. 2019;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-019-7818-6\u003c/span\u003e\u003cspan address=\"10.1186/s12889-019-7818-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam A, Nahar T, Siddiquee T, Toma AS, Hoque F, Hossain Z. Prevalence and determinants of utilizing skilled birth attendance during home delivery of pregnant women in India: Evidence from the Indian Demographic and Health Survey 2015\u0026ndash;16. PLoS One. 2024;19 3 March. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0295389\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0295389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThapa NR. Factors Associated with Skilled Birth Attendance in Nepal. Curriculum Dev J. 2024;32:125\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/cdj.v32i46.77010\u003c/span\u003e\u003cspan address=\"10.3126/cdj.v32i46.77010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThapa NR. Factors Associated with Skilled Birth Attendance in Nepal. Curriculum Dev J. 2024;32:125\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/cdj.v32i46.77010\u003c/span\u003e\u003cspan address=\"10.3126/cdj.v32i46.77010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNepal.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmer SB, Richards JL, Madhi SA, Tapia MD, Steinhoff MC, Aqil AR, et al. Three randomized trials of maternal influenza immunization in Mali, Nepal, and South Africa: Methods and expectations. Vaccine. 2015;33:3801\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.vaccine.2015.05.077\u003c/span\u003e\u003cspan address=\"10.1016/j.vaccine.2015.05.077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhakal MS, Dhakal S, Van Teijlingen E, Amalraj Raja E, Dhakal KB. Skilled Care at Birth among Rural Women in Nepal: Practice and Challenges. J HEALTH POPUL NUTR. 2011;29:371\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShah R. Nepal Demographic and Health Survey 2022 Ministry of Health and Population New ERA Ministry of Health and Population. 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbera M, Mariam AG, Belachew T. Predictors of Safe Delivery Service Mulumebet A. 95 PREDICTORS OF SAFE DELIVERY SERVICE UTILIZATION IN ARSI ZONE, SOUTH-EAST ETHIOPIA.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimeka Teferra A, Mazengia Alemu F, Meseret Woldeyohannes S. Institutional delivery service utilization and associated factors among mothers who gave birth in the last 12 months in Sekela District, North West of Ethiopia: A community-based cross sectional study. 2012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTessema ZT, Tesema GA. Pooled prevalence and determinants of skilled birth attendant delivery in East Africa countries: a multilevel analysis of Demographic and Health Surveys. Ital J Pediatr. 2020;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13052-020-00943-z\u003c/span\u003e\u003cspan address=\"10.1186/s13052-020-00943-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJacobs C, Moshabela M, Maswenyeho S, Lambo N, Michelo C. Predictors of antenatal care, skilled birth attendance, and postnatal care utilization among the remote and poorest rural communities of Zambia: A multilevel analysis. Front Public Health. 2017;5:FEB. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FPUBH.2017.00011\u003c/span\u003e\u003cspan address=\"10.3389/FPUBH.2017.00011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMati K, Adegoke KK, Michael-Asalu A, Salihu HM. Health insurance coverage and access to skilled birth attendance in Togo. Int J Gynecol Obstet. 2018;141:181\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ijgo.12449\u003c/span\u003e\u003cspan address=\"10.1002/ijgo.12449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell OMR, Calvert C, Testa A, Strehlow M, Benova L, Keyes E, et al. The scale, scope, coverage, and capability of childbirth care. Lancet. 2016;388:2193\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(16)31528-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)31528-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller S, Abalos E, Chamillard M, Ciapponi A, Colaci D, Comand\u0026eacute; D, et al. Beyond too little, too late and too much, too soon: a pathway towards evidence-based, respectful maternity care worldwide. Lancet. 2016;388:2176\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(16)31472-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)31472-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsegay Y, Gebrehiwot T, Goicolea I, Edin K, Lemma H, San Sebastian M. Determinants of antenatal and delivery care utilization in Tigray region, Ethiopia: a cross-sectional study. 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell OMR, Graham WJ. Series Maternal Survival 2 Strategies for reducing maternal mortality: getting on with what works. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.thelancet.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140\u003c/span\u003e\u003cspan address=\"10.1016/S0140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeidu AA, Ahinkorah BO, Agbaglo E, Oduro JK, Amoah A, Yaya S. Factors associated with the utilisation of skilled delivery services in Papua New Guinea: Evidence from the 2016\u0026ndash;2018 Demographic and Health Survey. Int Health. 2022;14:64\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/inthealth/ihab007\u003c/span\u003e\u003cspan address=\"10.1093/inthealth/ihab007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhinkorah BO, Seidu AA, Agbaglo E, Adu C, Budu E, Hagan JE, et al. Determinants of antenatal care and skilled birth attendance services utilization among childbearing women in Guinea: evidence from the 2018 Guinea Demographic and Health Survey data. BMC Pregnancy Childbirth. 2021;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-020-03489-4\u003c/span\u003e\u003cspan address=\"10.1186/s12884-020-03489-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam M, Yoshida S. MDG 5: How close are we to success? BJOG: An International Journal of Obstetrics and Gynaecology. 2009;116 SUPPL. 1:2\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1471-0528.2009.02335.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1471-0528.2009.02335.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFekadu M, Regassa N. Skilled delivery care service utilization in Ethiopia: Analysis of rural-urban differentials based on national demographic and health survey (DHS) data. Afr Health Sci. 2014;14:974\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ahs.v14i4.29\u003c/span\u003e\u003cspan address=\"10.4314/ahs.v14i4.29\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKibria GM, Al, Ghosh S, Hossen S, Barsha RAA, Sharmeen A, Uddin SMI. Factors affecting deliveries attended by skilled birth attendants in Bangladesh. Matern Health Neonatol Perinatol. 2017;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40748-017-0046-0\u003c/span\u003e\u003cspan address=\"10.1186/s40748-017-0046-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYakoob MY, Ali MA, Ali MU, Imdad A, Lawn JE, Van Den Broek N et al. The effect of providing skilled birth attendance and emergency obstetric care in preventing stillbirths. BMC Public Health. 2011;11 SUPPL. 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-11-S3-S7\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-11-S3-S7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMannah MT, Warren C, Kuria S, Adegoke AA. Opportunities and challenges in implementing community based skilled birth attendance strategy in Kenya. BMC Pregnancy Childbirth. 2014;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2393-14-279\u003c/span\u003e\u003cspan address=\"10.1186/1471-2393-14-279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerhan Y, Berhan A, SKILLED HEALTH PERSONNEL ATTENDED DELIVERY AS A PROXY INDICATOR FOR MATERNAL, AND PERINATAL MORTALITY. : A SYSTEMATIC REVIEW. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ejhs.v24i1.7S\u003c/span\u003e\u003cspan address=\"10.4314/ejhs.v24i1.7S\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJelka Z. \u0026Aring;hman Elisabeth. Neonatal and perinatal mortality: country, regional and global estimates 2004. World Health Organization; 2007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlvarez JL, Gil R, Hern\u0026aacute;ndez V, Gil A. Factors associated with maternal mortality in Sub-Saharan Africa: An ecological study. BMC Public Health. 2009;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-9-462\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-9-462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Spatial, Stratified, Determinants, SBA","lastPublishedDoi":"10.21203/rs.3.rs-7973803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7973803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Skilled birth assistance (SBA) is one of the determinants of maternal and neonatal health. Although there has been development in Nepal, there is still imbalance in SBA coverage in socioeconomic, obstetric, and geographical dimensions. This research sought to determine the major risk factors of SBA, spatial differences in SBA, and rural and urban differences in the determinants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e: This study used national representative data on Nepal Demographic and Health Survey (NDHS) 2022, which included women age 15-49 years with a last live birth within the last 5 years. The outcome variable categorized as SBA at delivery (skilled vs unskilled). Step wise survey-weighted multivariable logistic regression was used to assess the national and rural -urban determinants with fitted assessed by likelihood ratio tests, (AIC), (BIC), and result presented as adjusted odds ratios (aORs) with the 95 % confidence intervals (CI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: Among 2,785 women, 72.5% of women were received skill birth assistance (SBA). The uptake of SBA was highest among women aged 20-34 years, secondary or higher education, and richer households. Multiparity (aOR :0.47 ,95% CI:0.36–0.63). The obstetric variables such as 4 or more antenatal care visits (aOR 2.30, 95% CI 1.80-2.94) and caesarean section delivery (aOR: 3.86, 95% CI: 2.42-6.16) were significant predictors of SBA. Maternal age during first birth 20 years and above, increased SBA likelihood to a moderate extent (aOR :1.23,95% CI 1.00-1.50). SBA coverage in the provinces differed significantly with the highest level in Madhesh (≈18 .19%) and the smallest in Gandaki and Bagmati (≈9.11%). Caesarean delivery, ≥4 ANC visits, and residence in Lumbini were the most significant predictors in rural areas and Gandaki residence, caesarean delivery, ≥4 ANC visits and no desire of having additional children were the most significant predictors in urban areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Maternal, obstetric and socioeconomic factors have a strong impact on SBA in Nepal, and there are immense geographic and rural-urban differences. Specific actions taken against ANC uptake, parity, and provincial inequity: It is necessary to increase SBA coverage on the national level.\u003c/p\u003e","manuscriptTitle":"Determinants and Spatial Variation of Skilled Birth Assistance in Nepal: Insights from NDHS 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 12:04:06","doi":"10.21203/rs.3.rs-7973803/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T20:19:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T17:27:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164400612109228469199890952572699256427","date":"2026-03-30T08:58:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16904026687827288801634786802058300058","date":"2026-03-27T09:02:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T12:07:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63695546971644210273398258839240775563","date":"2025-11-18T15:18:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66794136716233685131426237471326745884","date":"2025-11-16T07:43:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T04:26:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T05:34:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-05T14:37:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-02T07:00:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-10-30T12:54:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0c169ae-16b1-456c-be59-c53b2f5b61ed","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T20:24:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 12:04:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7973803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7973803","identity":"rs-7973803","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}