Assessing the Impact of Early Marriage and Socioeconomic Determinants on Under-Five Morbidity: A Cross-Country Analysis in South Asia

Assessing the Impact of Early Marriage and Socioeconomic Determinants on Under-Five Morbidity: A Cross-Country Analysis in South Asia | 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 Assessing the Impact of Early Marriage and Socioeconomic Determinants on Under-Five Morbidity: A Cross-Country Analysis in South Asia Jakir Hossain, Abu Sayeed Md. Ripon Rouf, Muhammad Tareq, Md. Rokunuzzaman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7306650/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in BMC Pediatrics → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Early marriage and socioeconomic factors, which expose young mothers to early pregnancy under situations of adversity, as a result, dramatically raise the risk of children’s morbidity and perpetuate intergenerational cycles of poor health and disparity. Thus, this study is to assess the impact of early marriage and socioeconomic factors on children’s morbidity in South Asian countries using national survey data. Materials and Methods: This study utilized the most recent nationally representative Demographic and Health Survey (DHS) child datasets from five South Asian countries—Bangladesh, India, Pakistan, Afghanistan, and Nepal—comprising a total sample of 286,131 children. The study's outcome variable was the child morbidity. In addition to descriptive statistics, a two-stage binary logistic regression was used to analyze factors influencing child morbidity. Results: In South Asia, Pakistan had the highest prevalence of child morbidity at 44.38%, followed by Afghanistan at 42.25%, Bangladesh at 34.20%, Nepal at 28.33%, and India with the lowest at 17.72%. Binary logistic regression revealed key factors associated with under-five morbidity in South Asia. Children born to early-married mothers in Pakistan had a significantly higher risk of morbidity (OR = 1.43, 95% CI: 1.20-1.70). Higher morbidity was also associated with maternal secondary education in Pakistan (OR = 1.88, 95% CI: 1.40-2.53), eight or more antenatal care visits in Pakistan (OR = 1.75, 95% CI: 1.17-2.63), Afghanistan (OR = 2.23, 95% CI: 1.83-2.71), and female-headed households in India and Pakistan (OR = 1.14, 95% CI: 1.05-1.24). Breastfeeding was connected to higher child morbidity in Bangladesh, India, Pakistan, and Afghanistan. In contrast, higher maternal education was associated with a significant reduction in child morbidity in both India (OR = 0.81, 95% CI: 0.70–0.93) and Afghanistan (OR = 0.57, 95% CI: 0.45–0.72). Rural residence (OR = 0.80, 95% CI: 0.73–0.87) in Afghanistan, as well as wealth status in India (OR = 0.79, 95% CI: 0.72–0.87), were protective factors. Conclusion: These findings highlight the urgent need to delay early marriage and address socioeconomic disparities to reduce child morbidity in South Asia. Improving maternal education and access to healthcare is crucial for enhancing child health and well-being in the region. Early marriage Under-five morbidity Child health Socioeconomic determinants Binary logistic regression Introduction Early marriage, defined as marriage before the age of 18, remains a major social issue globally, particularly in developing countries. In low- and middle-income nations, it disproportionately impacts females and violates their rights. According to UNICEF, around 12 million girls are still married before turning 18 every year. The highest rates are found in South Asia (30%), Latin America (25%), and Sub-Saharan Africa (38%) [ 1 ]. According to data from 41 low-income nations, the global rate of early marriage is approximately 30%, with Niger having the highest rate at 75%[ 2 ], [ 3 ]. In India, 22% of women aged 20 to 24 gave birth before age 18, and 44.5% were married before 18. Early motherhood is linked to higher risks of stillbirth, neonatal death, low birth weight, and illness [ 4 ]. By 2007, 74.9% of Pakistani women aged 20 to 24 had at least one child, with 31.6% giving birth in the first year of their marriage, and 50% being married before turning 18[ 5 ]. In 2018, over one-third of women in Pakistan and Afghanistan were married before turning 18, influenced by conservative Islamic cultural norms [ 6 ]. In Nepal, 33% of women aged 20–24 married before age 18, and 8% before 15 years, even though the legal minimum age is 20 years old, or 18 years with parental agreement[ 7 ]. Child marriage damages both mothers and their children under the age of five, raising the risk of morbidity. In 2017, over 15,000 children under the age of five died every day worldwide[ 1 ]. These risks are associated with adolescent vulnerability, such as maternal sadness, poverty, and hunger. Antenatal care, skillful delivery, and child vaccines are all less common due to limited education and healthcare availability[ 8 ]. Young married girls are also at risk because of their shorter birth spacing and higher risk of HIV infection[ 9 ]. To determine the influence of early marriage on infant morbidity, it is critical to distinguish between health hazards associated with early childbirth and the socioeconomic determinants and structural vulnerabilities that young mothers experience[ 5 ]. Numerous social, demographic, and environmental factors affect children's nutrition and anemia; among these, the mother's age at marriage is a significant predictor of birth outcomes, nutrition, and cognitive development[ 10 ]. Respiratory infections and diarrhea are most common in the first two years, affecting growth and increasing child mortality.[ 11 ]. Approximately 40–60% of children worldwide receive proper care for diarrhea and ARIs, which remain top killers of children under five[ 12 ]. The prevalence of common illnesses, including AIDS, fever, cough, diarrhea, and malaria, keeps childhood morbidity high[ 13 ]. Understanding infection origins and transmission is critical for effective control.[ 14 ]. Child health is strongly influenced by socioeconomic factors such as parental education, wealth, residence, healthcare access, and maternal nutrition[ 15 ]. Effective approaches include teaching and supporting girls, boosting female education quality, providing financial incentives, and engaging parents and community leaders to discourage early marriage[ 16 ]. Girls who stay in school longer have a lower chance of getting pregnant[ 17 ]. Improving child health and empowering females, while addressing socioeconomic issues, is critical in low-income countries for reducing early marriage and boosting survival and growth.[ 18 ][ 19 ]. The extant literature highlights the noteworthy influence of early marriage on the rates of morbidity among children in South Asia. Although early marriage and its negative effects on child health have been examined, there remains a significant gap in understanding how early marriage, combined with various socioeconomic factors, specifically affects under-five morbidity in South Asia. This work fills a gap by investigating the combined impact of early marriage and socioeconomic variables on under-five morbidity using an analysis of the DHS child dataset from South Asia. Objectives of the study The intention of this study is to find out the impact of early maternal marriage and socioeconomic factors on sickness among South Asian children. The specific objectives are: To assess the prevalence of child illness in several South Asian nations. To mark out the impact of notable socioeconomic factors on morbidity in children. To analyze the effect of early marriage and socioeconomic determinants on child morbidity using statistical models. Materials and Methods Study design We used the analysis based on secondary data. These surveys employed a two-stage stratified sampling technique. This study utilized child recode datasets from recent Demographic and Health Surveys in five South Asian countries, focusing exclusively on children under the age of five. In Bangladesh, data from the 2017–18 BDHS included 8,772 children[ 20 ], in India, the 2019–21 NFHS covered 230,870 children[ 21 ], in Pakistan, the 2017–18 PDHS included 10,494 children[ 22 ], in Afghanistan, the 2015 AfDHS surveyed 31,802 children[ 23 ], and in Nepal, the 2022 NDHS involved 5,193 children[ 24 ]. The present study only included children under the age of five from these nationally representative household surveys. Target Variable The study's main goal variable is the morbidity indicators of children under five. In the two weeks before the survey, the three variables that comprised the morbidity indicators were fever, diarrhea, and acute respiratory infections (ARIs). Thus, all outcome variables were recoded as yes = 1 and no = 0 to do the analysis. Morbidity Indicators Diarrhea in the last two weeks: This was evaluated by asking the kid if they had experienced diarrhea in the previous two weeks, with "1" coding "yes" and "0" coding "no". Fever within the previous two weeks: This was evaluated by asking if the kid had a fever during that time, with "1" coding "yes" and "0" coding "no". Two questions measuring whether the kid had experienced a cough and rapid breathing in the previous two weeks were used to determine whether the child had an acute respiratory infection (ARI), with "1" coding "yes" and "0" coding "no". And then we recoded the morbidity variable with "1" coding "yes" and "0" coding "no". Independent Variables The main predictor variable in this study is early marriage, which is defined as an adult marriage when the respondent was 18 years of age or older and an early marriage when the respondent was under 18. Additionally, several socioeconomic and demographic factors are incorporated into the study to examine their relationship to early marriage, and they serve as controlled variables in the adjusted logistic regression model. There are several independent variables such as age at first marriage, child sex, multiple births, birth order, maternal age, maternal education, paternal education, mass media exposure, number of ANC visits, delivery by caesarean section, place of delivery, maternal BMI, sex of household head, wealth index, and place of residence. Statistical Analysis The impact of early marriage and socioeconomic determinants on children's morbidity was analyzed through descriptive statistics and binary logistic regression methods to assess potential risks. The Pearson chi-square test was used to show the association between categorical variables. Binary logistic regression model has also been utilized to assess the impact of early marriage and socioeconomic determinants on child morbidity in children under five. Relationships between factors were quantified by computing adjusted odds ratios with 95% confidence intervals. There were controlling variables in adjusted analyses related to child morbidity. Using Microsoft Word, Microsoft Excel, and SPSS Version 25, all data were weighted and examined. Binary Logistic Regression Model Binary logistic regression is an appropriate statistical method for modeling the relationship between a dichotomous dependent variable and one or more independent variables, which may be continuous or categorical. In this study, binary logistic regression was employed to estimate the likelihood of the binary outcome based on a set of explanatory variables. The logistic regression model estimates the probability of occurrence of the event of interest by fitting data to a logistic function. The model is defined as: $$\:\text{log}\left(\frac{p}{1-p}\right)={\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}\:+\dots\:+{\beta\:}_{k}{X}_{k}$$ where \(\:p\) is the probability of the event, \(\:{X}_{1}\) , \(\:{X}_{2},\dots\:,{X}_{k}\) are independent variables, and \(\:{\beta\:}_{0},{\beta\:}_{1},{\beta\:}_{2},\dots\:,{\beta\:}_{k}\:\) are the model coefficients estimated through maximum likelihood estimation (MLE). The coefficients are interpreted in terms of odds ratios (OR), where OR \(\:={e}^{\beta\:}\) . An odds ratio greater than 1 indicates a positive association with the outcome, whereas an odds ratio less than 1 indicates a negative association. Results Prevalence of child morbidity: Showing the child morbidity scenario among children aged under five years across five South Asian countries. Pakistan had the highest percentage, 44.5% of child morbidity in South Asia, while India had the lowest, 17.7%. Of the children from Afghanistan, 42.3% had child morbidity, followed by Bangladesh at 34.2%, and Nepal at 28.3% as shown in Table 1 . Table 1 Prevalence of child morbidity in South Asian countries. Country Total individuals No of illness Prevalence (%) Bangladesh 8772 3000 34.2 India 230870 40903 17.7 Pakistan 10494 4665 44.5 Afghanistan 31802 13437 42.3 Nepal 5193 1471 28.3 Association between independent variables and morbidity outcome: The findings of Pearson chi-square tests, which evaluated the bivariate relationships between early maternal marriage, important socioeconomic factors, and the rate of morbidity in children under five, are shown in Table 2 . In South Asia, 35.2% of women in Bangladesh, 19.6% in India, and 43.3% in Afghanistan marry before the age of 18, demonstrating that early marriage is common throughout the region. In Bangladesh, 35.3% of male children and 32.9% of female children have morbidity, while in India, the rates are 18.3% for male children and 17.1% for female children. In India, 17.8% of children with morbidity are single births, whereas 13.2% are twins or higher-order multiples. In Pakistan, 44.8% of births are singletons, and 30.7% are multiples. In Afghanistan, 42.4% of births are singletons, and 34.8% are multiples. In Nepal, 28.5% of births are singletons, and 15.2% are multiples. Morbidity affects 17.5% of first-born infants in India, 42.8% of second- or third-born children in Afghanistan, and 29.6% of first-born children in Nepal. The prevalence of morbidity among breastfeeding children is 40.4% in Bangladesh, 21.8% in India, 54.0% in Pakistan, 48.8% in Afghanistan, and 31.4% in Nepal, while among non-breastfeeding children, the rates are 28.7%, 14.3%, 40.5%, 38.9%, and 25.3%, respectively. Morbidity affects 39.3% of children born to women aged 15–18 years in Bangladesh, 25.4% in India, and 55.2% in Pakistan. Child morbidity rates differ by maternal education level: in Bangladesh, 31.6% for uneducated and 30.4% for secondary-educated mothers; in India, 16.7% for uneducated and 15.2% for higher-educated mothers; in Pakistan, 39.9% for uneducated and 51.5% for secondary-educated mothers; in Afghanistan, 46.1% for primary and 33.5% for higher-educated mothers; and in Nepal, 24.8% for uneducated and 30.4% for secondary-educated mothers. Children with uneducated fathers have a morbidity rate of 16.8% in India, 41.9% in Pakistan, and 43.0% in Afghanistan. 17.4% of mothers in India, 47.0% in Pakistan, and 29.7% in Nepal have access to mass media, which is critical for raising knowledge regarding child health and morbidity. The incidence of child morbidity among mothers with no ANC visits is 23.5% in India, 44.3% in Pakistan, and 40.7% in Afghanistan, but for those with 8 or more ANC visits, the rates are 19.5% in India, 58.1% in Pakistan, and 1.4% in Afghanistan. Morbidity among children born via cesarean section is 18.5% in India, 47.7% in Pakistan, and 49.0% in Afghanistan. In Pakistan, 41.7% of deliveries are normal, and 45.8% take place at health facilities. Child morbidity rates among mothers with an underweight BMI are 35.9% in Bangladesh and 19.7% in India, compared to 32.4% and 16.7% for mothers with an overweight or obese BMI, respectively. In India, child morbidity rates are 17.4% for male-headed households and 19.4% for female-headed households. Child morbidity rates are 36.0% in Bangladeshi middle-class households, 19.1% in Indian poor households, and 45.8% in Pakistani wealthy households. Child morbidity rates in urban areas of Bangladesh are 31.9%, 16.1% in urban India, 45.1% in urban Afghanistan, and 29.4% in urban Nepal. Table 2 Association of early marriage and socioeconomic determinants with child morbidity: Bivariate Analysis Explanatory Variables Child morbidity Bangladesh India Pakistan Afghanistan Nepal Yes N (%) No N (%) P-value Yes N (%) No N (%) P-value Yes N (%) No N (%) P-value Yes N (%) No N (%) P-value Yes N (%) No N (%) P-value Age at first marriage Early marriage Adult marriage 2204(35.2) 796(31.7) 4053(64.8) 1719(68.3) < 0.001 15118(19.6) 25695(16.8) 62157(80.4) 127501(83.2) < 0.001 1567(43.9) 3097(44.8) 2005(56.1) 3821(55.2) 0.380 7095(43.3) 6297(41.2) 9290(56.7) 8996(58.8) < 0.001 684(27.4) 786(29.1) 1810(72.6) 1911(70.9) 0.170 Child sex Male Female 1620(35.3) 1380(32.9) 2963(64.7) 2809(67.1) < 0.018 21964(18.3) 18939(17.1) 97995(81.7) 91971(82.9) < 0.001 2355(44.4) 2310(44.2) 2908(55.3) 2921(55.8) 0.545 7016(42.7) 6421(41.7) 9398(573) 8966(58.3) 0.067 800(29.3) 671(27.2) 1929(70.7) 1793(72.8) 0.096 Child twin Single birth Multiple births 2950(34.3) 50(30.9) 5660(65.7) 112(69.1) 0.366 40372(17.8) 531(13.2) 186486(82.2) 3481(86.8) < 0.001 4587(44.8) 78(30.7) 5653(55.2) 176(69.3) < 0.001 13251(42.4) 187(34.8) 18014(57.6) 350(65.2) < 0.001 1461(28.5) 10(15.2) 3666(71.5) 56(84.8) < 0.017 Birth order First Second or third Fourth and up 1112(32.8) 1495(35.0) 393(35.6) 2283(67.2) 2779(65.0) 710(64.4) 0.070 15803(17.5) 19994(17.7) 5101(18.5) 74524(82.5) 92916(82.3) 22498(81.5) < 0.001 1157(45.4) 1801(44.5) 1693(43.8) 1391(54.6) 2250(55.5) 2174(56.2) 0.439 2449(40.4) 4440(42.8) 6455(42.5) 3619(59.6) 5924(57.2) 8736(57.5) < 0.005 605(28.5) 736(29.6) 130(22.3) 1518(71.5) 1749(70.4) 454(77.7) < 0.002 Currently breastfeeding No Yes 1328(28.7) 1672(40.4) 3307(71.3) 2466(59.6) < 0.001 18082(14.3) 22822(21.8) 108153(85.7) 81814(78.2) < 0.001 2993(40.5) 1672(54.0) 4403(59.5) 1426(46.0) < 0.001 8213(38.9) 5225(48.8) 12885(61.1) 5479(51.2) < 0.001 658(25.3) 813(31.4) 1946(74.7) 1776(68.6) < 0.001 Mother's age 15–18 19–49 252(39.3) 2748(33.8) 389(60.7) 5383(66.2) < 0.005 610(25.4) 40293(17.6) 1793(74.6) 188173(82.4) < 0.001 74(55.2) 4591(44.3) 60(44.8) 5769(55.7) < 0.012 241(43.1) 13196(42.2) 318(56.9) 18046(57.8) 0.678 46(31.1) 1425(28.2) 102(68.9) 3620(71.8) 0.451 Maternal education No education Primary Secondary Higher 204(31.6) 866(34.3) 1518(35.7) 411(30.4) 441(68.4) 1661(65.7) 2731(64.3) 939(69.6) < 0.002 8255(16.7) 5212(18.3) 21938(18.7) 5498(15.2) 41051(83.3) 32222(81.7) 95093(81.3) 30601(84.8) < 0.001 2065(39.9) 832(47.7) 1160(51.5) 607(46.1) 3113(60.1) 913(52.3) 1091(48.5) 711(53.9) < 0.001 11168(42.0) 1154(46.1) 952(42.5) 164(33.5) 15399(58.0) 1350(53.9) 1289(57.5) 326(66.5) < 0.001 286(24.8) 517(28.3) 606(30.4) 62(28.8) 866(75.2) 1313(71.7) 1389(69.6) 153(71.2) < 0.011 Father’s education No education Primary Secondary and Higher Don’t know 435(33.4) 1026(34.7) 1485(34.2) 4(14.3) 866(66.6) 1935(65.3) 2854(65.8) 24(85.7) 0.136 892(16.8) 905(19.4) 4553(18.2) 35(22.6) 4429(83.2) 3750(80.6) 20480(81.8) 120(77.4) < 0.003 1283(41.9) 810(45.2) 2491(45.6) 6(21.4) 1778(58.1) 983(54.8) 2967(54.4) 22(78.6) < 0.001 7823(42.7) 1981(43.0) 3510(41.0) 93(37.2) 10477(57.3) 2625(57.0) 5042(59.0) 157(62.8) < 0.014 143(24.4) 609(29.8) 677(28.1) 30(30.0) 442(75.6) 1433(70.2) 1735(71.9) 70(70.0) 0.079 Mass media exposure Not Exposed Exposed 1384(34.8) 1616(33.7) 2598(65.2) 3175(66.3) 0.313 20755(18.0) 20149(17.4) 94384(82.0) 95583(82.6) < 0.001 2261(42.1) 2404(47.0) 3114(57.9) 2716(53.0) < 0.001 6405(42.2) 7032(42.3) 8770(57.8) 9594(57.7) 0.874 776(27.2) 695(29.7) 2079(72.8) 1643(70.3) < 0.043 Number of ANC No ANC Less than 8 8+ 168(41.5) 1599(39.1) 197(35.3) 237(58.5) 2488(60.9) 361(64.7) 0.119 3051(23.5) 25148(19.6) 6563(19.5) 9905(76.5) 103185(80.4) 27095(80.5) < 0.001 374(44.3) 2504(50.8) 544(58.1) 470(55.7) 2425(49.2) 393(41.9) < 0.001 3199(40.7) 5954(52.7) 270(57.3) 4655(59.3) 5353(47.3) 201(42.7) < 0.001 20(26.9) 814(31.3) 56(33.7) 55(73.3) 1789(68.7) 110(66.3) 0.547 Delivery by caesarean section No Yes 1378(38.4) 660(37.8) 2206(61.6) 1087(62.2) 0.637 31715(17.5) 9188(18.5) 149521(82.5) 40446(81.5) < 0.001 3539(43.5) 1116(47.7) 4604(56.5) 1223(52.3) < 0.001 13000(42.1) 422(49.0) 17864(57.9) 439(51.0) < 0.001 735(29.4) 190(33.2) 1764(70.6) 382(66.8) 0.074 Place of delivery Respondents Home With Health Facility 1051(39.4) 988(37.0) 1619(60.6) 1679(63.0) 0.081 4647(18.0) 36256(17.7) 21201(82.0) 168765(82.3) 0.243 1475(41.7) 3190(45.8) 2058(58.3) 3768(54.2) < 0.001 6917(42.7) 6515(42.1) 9271(57.3) 8966(57.9) 0.245 166(27.3) 758(30.8) 443(72.7) 1704(69.2) 0.089 Mother’s BMI Underweight Normal Overweight/obese 421(35.9) 1734(35.5) 698(32.4) 753(64.1) 3152(64.5) 1456(67.6) < 0.030 8161(19.7) 22028(17.6) 6769(16.7) 33361(80.3) 103267(82.4) 33844(83.3) < 0.001 182(44.6) 723(48.3) 688(46.3) 226(55.4) 774(51.7) 797(53.7) 0.330 N/A 94(27.2) 443(29.2) 208(32.6) 251(72.8) 1076(70.8) 431(67.4) 0.159 Sex of household head Male Female 2582(34.0) 418(35.6) 5017(66.0) 755(64.4) 0.266 34098(17.4) 6805(19.4) 161730(82.6) 28235(80.6) < 0.001 4130(44.2) 535(46.1) 5204(55.8) 626(53.9) 0.236 13279(42.2) 158(44.6) 18168(57.8) 196(55.4) 0.362 1013(28.6) 457(27.8) 2535(71.4) 1187(72.2) 0.575 Wealth index Poor Middle Rich 1276(34.8) 593(36.0) 1131(32.7) 2388(65.2) 1053(64.0) 2331(67.3) < 0.035 20388(19.1) 8233(18.3) 12282(15.6) 86552(80.9) 36868(81.7) 66546(84.4) < 0.001 1899(42.3) 1011(46.4) 1755(45.8) 2587(57.7) 1167(53.6) 2076(54.2) < 0.001 5289(41.9) 2886(42.6) 5262(42.5) 7343(58.1) 3892(57.4) 7129(57.5) 0.526 646(27.5) 332(30.8) 493(27.9) 1700(72.5) 747(69.2) 1274(72.1) 0.132 Place of residence Urban Rural 769(31.9) 2231(35.1) 1644(68.1) 4129(64.9) < 0.005 9922(16.1) 30981(18.3) 51606(83.9) 138361(81.7) < 0.001 1533(45.7) 3132(43.8) 1819(54.3) 4011(56.2) 0.070 3269(45.1) 10168(41.4) 3977(54.9) 14387(58.6) < 0.001 991(29.4) 480(26.3) 2376(70.6) 1346(73.7) < 0.016 Note: Table 2 shows the bivariate associations between early maternal marriage, socioeconomic and demographic characteristics, and morbidity among children under the age of five. Pearson's Chi-squared test was used to analyze the connections. A p-value of less than 0.05 indicates statistical significance. N/A = Not Available Significant risk factors for under-five child morbidity across South Asian countries: To better understand the determinants of under-five morbidity, Table 3 presents the adjusted odds ratios (OR) derived from binary logistic regression models across five South Asian countries. The study finds that various child, mother, and household-level characteristics are strongly linked with the risk of morbidity. The following is a descriptive overview that focuses on the factors that exhibited a statistically significant positive association with child morbidity, provided by the nation. Breastfeeding was the only significant risk factor in Bangladesh, with children being 29% more likely to suffer morbidity (AOR = 1.29, 95% CI: 1.09–1.52), probably due to reverse causation. In India, morbidity was significantly higher among breastfed children (AOR = 1.32, 95% CI: 1.24–1.42), children of mothers with secondary education (AOR = 1.12, 95% CI: 1.01–1.24), fathers with secondary/higher education (AOR = 1.16, 95% CI: 1.04–1.29), those exposed to mass media (AOR = 1.08, 95% CI: 1.01–1.16), cesarean births (AOR = 1.09, 95% CI: 1.01–1.18), and children from female-headed households (AOR = 1.14, 95% CI: 1.05–1.24), and Adult marriage decreased the chance of morbidity (AOR = 0.82, 95% CI: 0.77–0.88), as did maternal higher education (AOR = 0.81, 95% CI: 0.70–0.93), rural residency (AOR = 0.87, 95% CI: 0.80–0.94), and normal maternal BMI. In Pakistan, female children were more likely to have morbidity (AOR = 1.43, 95% CI: 1.20–1.70), whereas maternal secondary education (AOR = 1.88, 95% CI: 1.40–2.53) and eight or more antenatal care (ANC) visits (AOR = 1.75, 95% CI: 1.17–2.63) were also linked with increased risk. In Afghanistan, breastfed children (AOR = 1.07, 95% CI: 1.01–1.13), children of older mothers (19–49 years) (AOR = 1.22, 95% CI: 1.01–1.49), and those whose mothers had 8 or more ANC visits (AOR = 2.23, 95% CI: 1.83–2.71) had significantly higher morbidity rates, and protective factors against child morbidity included adult marriage (AOR = 0.87, 95% CI: 0.82–0.92), multiple births (AOR = 0.62, 95% CI: 0.47–0.83), maternal higher education (AOR = 0.57, 95% CI: 0.45–0.72), delivery at a health facility (AOR = 0.78, 95% CI: 0.73–0.83), and rural residence (AOR = 0.80, 95% CI: 0.73–0.87). In Nepal, none of the variables investigated had statistically significant relationships with under-five morbidity. Table 3 Country-wise binary logistic regression estimates of factors associated with under-five morbidity in South Asia Explanatory Variables Child morbidity Bangladesh India Pakistan Afghanistan Nepal ORs 95% CI P-value ORs 95% CI P-value ORs 95% CI P-value ORs 95% CI P-value ORs 95% CI P-value upper-lower upper-lower upper-lower upper-lower upper-lower Age at first marriage Early marriage Adult marriage 1 0.90 1 0.78–1.04 0.145 1 0.82*** 1 0.77–0.88 0.000 1 0.83 1 0.67–1.02 0.075 1 0.87*** 1 0.82–0.92 0.000 1 0.99 1 0.76–1.30 0.939 Child sex Male Female 1 0.91 1 0.81–1.02 0.102 1 0.92* 1 0.87–0.98 0.013 1 1.43*** 1 1.20–1.70 0.000 1 0.97 1 0.92–1.03 0.334 1 1.01 1 0.80–1.28 0.909 Child twin Single birth Multiple births 1 1.12 1 0.62-2.00 0.712 1 0.84 1 0.59–1.20 0.316 1 0.58 1 0.29–1.18 0.132 1 0.62*** 1 0.47–0.83 0.001 1 0.69 1 0.13–3.79 0.671 Birth order First Second or third Fourth and up 1 1.08 1.16 1 0.94–1.25 0.93–1.45 0.287 0.194 1 0.90** 0.76*** 1 0.84–0.97 0.68–0.86 0.005 0.000 1 1.14 1.10 1 0.88–1.47 0.84–1.44 0.327 0.494 1 0.97 0.93 1 0.88–1.06 0.85–1.02 0.505 0.113 1 1.15 0.84 1 0.88–1.50 0.52–1.36 0.321 0.473 Currently breastfeeding No Yes 1 1 .29*** 1 1.09–1.52 0.003 1 1.32*** 1 1.24–1.42 0.000 1 1.59*** 1 1.33–1.91 0.000 1 1.07* 1 1.01–1.13 0.026 1 1.44 1 0.90–2.32 0.131 Mother's age 15–18 19–49 1 0.96 1 0.77–1.19 0.696 1 0.98 1 0.76–1.25 0.855 1 2.18* 1 1.09–4.37 0.028 1 1.22* 1 1.01–1.49 0.046 1 0.85 1 0.45–1.59 0.608 Maternal education No education Primary Secondary Higher 1 0.86 0.91 0.79 1 0.66–1.12 0.69–1.19 0.57–1.08 0.252 0.471 0.140 1 1.08 1.12* 0.81*** 1 0.96–1.22 1.01–1.24 0.70–0.93 0.217 0.029 0.003 1 1.37* 1.88*** 1.35 1 1.03–1.82 1.40–2.53 0.95–1.93 0.029 0.000 0.096 1 1.15* 0.97 0.57*** 1 1.03–1.28 0.86–1.09 0.45–0.72 0.013 0.937 0.000 1 1.03 1.20 1.85 1 0.70–1.52 0.78–1.87 0.95–3.59 0.870 0.407 0.071 Father’s education No education Primary Secondary and Higher Don’t know 1 1.04 1.04 0.28 1 0.86–1.27 0.84–1.27 0.06–1.23 0.675 0.744 0.091 1 1.07 1.16** 1.33 1 0.94–1.21 1.04–1.29 0.85–2.08 0.325 0.009 0.220 1 0.93 0.80 1.24 1 0.70–1.23 0.63–1.03 0.16–9.58 0.595 0.086 0.839 1 0.98 0.97 0.76 1 0.90–1.07 0.91–1.05 0.55–1.05 0.637 0.479 0.099 1 0.90 1.01 1.26 1 0.55–1.42 0.61–1.67 0.52–3.06 0.616 0.983 0.612 Mass media exposure Not Exposed Exposed 1 1.06 1 0.93–1.22 0.390 1 1.08* 1 1.01–1.16 0.047 1 1.19 1 0.97–1.46 0.099 1 0.99 1 0.93–1.05 0.675 1 0.90 1 0.70–1.14 0.346 Number of ANC No ANC Less than 8 8+ 1 0.98 0.89 1 0.78–1.23 0.66–1.19 0.881 0.435 1 0.85** 0.84* 1 0.75–0.96 0.73–0.97 0.010 0.014 1 1.51** 1.75** 1 1.12–2.05 1.17–2.63 0.008 0.007 1 1.73*** 2.23*** 1 1.63–1.85 1.83–2.71 0.000 0.000 1 1.13 1.28 1 0.51–2.49 0.51–3.21 0.770 0.601 Delivery by caesarean section No Yes 1 1.07 1 0.91–1.30 0.369 1 1.09* 1 1.01–1.18 0.028 1 0.86 1 0.69–1.09 0.210 1 1.17 1 0.99–1.38 0.063 1 1.08 1 0.79–1.47 0.644 Place of delivery Respondents Home With Health Facility 1 0.94 1 0.79–1.12 0.478 1 0.96 1 0.86–1.07 0.453 1 0.72** 1 0.57–0.90 0.005 1 0.78*** 1 0.73–0.83 0.000 1 1.06 1 0.76–1.48 0.745 Mother’s BMI Underweight Normal Overweight/obese 1 0.92 0.83 1 0.78–1.08 0.68–1.03 0.303 0.084 1 0.86*** 0.87** 1 0.80–0.93 0.78–0.97 0.000 0.009 1 1.16 1.01 1 0.86–1.56 0.74–1.37 0.327 0.979 N/A 1 1.20 0.96 1 0.85–1.69 0.64–1.45 0.295 0.863 Sex of household head Male Female 1 1.01 1 0.84–1.20 0.970 1 1.14*** 1 1.05–1.24 0.003 1 0.95 1 0.71–1.27 0.716 1 1.05 1 0.81–1.36 0.715 1 0.87 1 0.67–1.12 0.272 Wealth index Poor Middle Rich 1 1.05 0.97 1 0.88–1.25 0.81–1.15 0.586 0.691 1 0.89* 0.79*** 1 0.82–0.98 0.72–0.87 0.014 0.000 1 0.98 0.85 1 0.75–1.28 0.62–1.15 0.875 0.286 1 0.94 0.97 1 0.87–1.01 0.90–1.06 0.096 0.505 1 0.95 0.89 1 0.69–1.31 0.66–1.21 0.750 0.466 Place of residence Urban Rural 1 1.09 1 0.93–1.26 0.286 1 1.03 1 0.95–1.12 0.529 1 0.95 1 0.77–1.19 0.674 1 0.80*** 1 0.73–0.87 0.000 1 0.97 1 0.75–1.25 0.803 Note: * Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) are displayed. Each model is stratified by nation and includes predictors at the child, mother, and household levels. The table indicates reference categories. AORs with *p < 0.05, **p < 0.01, and ***p < 0.001 are statistically significant. 1 = Reference category, and N/A = Not Available. Discussion This study conducts a robust, cross-country analysis of how early marriage and socioeconomic determinants affect under-five child morbidity in five South Asian countries: Bangladesh, India, Pakistan, Afghanistan, and Nepal. We used coordinated, nationally representative DHS data and binary logistic regression to identify critical child, maternal, and household-level factors that are significantly associated with recent childhood morbidity. The study assessed the association between socio-demographic characteristics and the outcome of child health[ 26 ]. Early maternal marriage (< 18 years) is substantially linked with increased under-five morbidity in India and Afghanistan. However, the direction of the association shows decreased odds. This could be due to confounding or reporting biases, as previous research has demonstrated that early marriage affects maternal autonomy and access to child health services[ 5 ],[ 19 ]. The early married child was significantly associated with an increased likelihood of under-five child morbidity than the adult married child[ 27 ]. In India, female children had reduced odds of morbidity, but in Pakistan, female children had greater odds, indicating that sex-based disparities in treatment or disease vulnerability differ by environment. It explicitly differentiates morbidity rates between boys and girls[ 1 ],[ 6 ],[ 5 ],[ 8 ]. In addition, multiple births were linked with significantly decreased morbidity in Afghanistan. Multiple births are substantially linked to greater rates of low birth weight and preterm birth, two factors that considerably exacerbate these children's morbidity[ 28 ],[ 29 ]. Birth order appears to have a strong impact on the parental treatment of child morbidity, especially when second- and third-born born are compared by child morbidity. Based on birth order, parental health investments varied significantly[ 30 ],[ 31 ]. Interestingly, children who were currently breastfeeding had significantly increased risks of morbidity in all countries, including India and Pakistan. This paradoxical result may represent reverse causality: women breastfeed longer in reaction to child illness[ 32 ]. Breastfeeding, especially exclusive breastfeeding, is linked to infant morbidity and infection risks[ 33 ],[ 34 ],[ 35 ]. Maternal age yielded conflicting results. In Pakistan, older women (19–49) were substantially more likely to report child morbidity, whereas in Afghanistan, the increase was small. These findings may reflect aging-related differences in birth spacing, health literacy, or maternal weariness. Other research found similar connections[ 36 ]. Maternal education was significantly beneficial in India and Afghanistan, according to global evidence that educated mothers are more likely to participate in effective health-seeking and disease preventive behaviors[ 37 ]. In contrast, paternal education was only substantially linked with morbidity in India, which could be attributed to better health awareness and reporting. This emphasizes how crucial elements like access to healthcare are[ 38 ]. However, depending on additional variables like geographic location and economic standing, the effect of schooling on child morbidity can differ[ 39 ],[ 40 ]. The habit of child feeding may benefit from the presence of educated parents[ 41 ]. In India, mass media exposure was related to increased morbidity, which may indicate that media-literate mothers are more likely to notice and report disease signs[ 42 ],[ 43 ]. Antenatal care (ANC) had a wide range of effects. In India, ≥ 8 ANC visits were protective, whereas in Pakistan and Afghanistan, more ANC was strangely related with increased morbidity. This may reflect that women with unwell children were more likely to obtain ANC, or that the quality of ANC treatment is inadequate in some circumstances[ 44 ]. The best maternal and fetal outcomes are associated with antenatal care (ANC)[ 45 ],[ 46 ]. Institutional delivery significantly reduced morbidity in Pakistan and Afghanistan, highlighting the importance of professional medical care at birth in mitigating early childhood disease risks. The immune system may develop differently in babies born with CS, increasing the risk of allergies, asthma, and a less diverse gut microbiota[ 47 ],[ 48 ]. Children of mothers who were born in Health facilities were less likely to have child morbidity[ 49 ]. Maternal BMI (normal range) was related to lower morbidity only in India, which may reflect the country's nutrition transition and expanding urban-rural dietary disparities[ 50 ]. Underweight mothers have a higher chance of having low birth weight and small-for-gestational-age babies, which are connected to higher morbidity[ 51 ],[ 52 ]. Premature birth, newborn asphyxia, fetal overgrowth, and increased neonatal morbidity are among the negative outcomes that are linked to higher maternal BMI[ 53 ],[ 54 ]. Additionally, from households where the household head was female were more likely to have child morbidity[ 55 ]. Wealth index likewise had a high protective impact in India, but the link was modest or nonsignificant in Bangladesh, Pakistan, and Nepal. This emphasizes the importance of taking into account country-specific health financing and access limits when understanding the impact on health outcomes[ 56 ], [ 57 ]. Finally, rural living was unexpectedly protective in Afghanistan, which could be attributed to reduced disease reporting, less environmental exposure, or changes in health-seeking behaviors. There was no consistent correlation in the other countries[ 58 ],[ 59 ]. Taken together, our findings demonstrate that early marriage is a significant contextual predictor of under-five morbidity, although its effects are frequently moderated or changed by maternal education, healthcare utilization, and household wealth. The study emphasizes the need for multisectoral measures, such as health system strengthening, education promotion, and gender equity reforms, in improving child health outcomes in South Asia. Strengths and Limitations This study has various advantages, including a comprehensive cross-country analysis employing nationally representative data, which improves the generalizability and validity of conclusions using robust statistical approaches. It contributes to the current literature by emphasizing the importance of maternal education and prenatal care in child health. However, drawbacks include the use of cross-sectional data, which restricts causal inference, potential self-reported answer bias, cross-country data collection errors, and unmeasured confounders. Future studies should look into longitudinal designs and cultural and policy factors on child morbidity. Conclusion This study looked at the effects of early marriage and socioeconomic determinants on under-five illness in South Asia, and it found strong links between early maternal marriage, low education, low socioeconomic level, and increased child morbidity. The findings emphasize the significance of maternal education, delayed marriage, and improved access to prenatal and healthcare services in lowering child health disparities. Despite constraints such as cross-country data inconsistency, the study provides useful insights for policymakers. Future studies should investigate region-specific trends and community-based measures to mitigate the effects of early marriage on child health outcomes. Abbreviations DHS Demographic and Health Survey BDHS Bangladesh Demographic and Health Survey NFHS National Family Health Survey PDHS Pakistan Demographic and Health Survey AfDHS Afghanistan Demographic and Health Survey NDHS Nepal Demographic and Health Survey HIV Human Immunodeficiency Virus AIDS Acquired Immunodeficiency Syndrome ARI Acute Respiratory Infection ANC Antenatal Care BMI Body Mass Index SPSS Statistical Package for the Social Sciences MLE Maximum Likelihood Estimation Declarations Ethical approval and consent to participate This study's foundation examined publicly accessible survey data sets stripped of all respondent-identifying information. The relevant Ethics Committees in Bangladesh, India, Pakistan, Afghanistan, Nepal, and ICF Macro at Calverton, USA, approved this survey. The Demographic and Health Surveys (DHS) program data archivist permitted the authors to download the dataset used in this investigation ( https://dhsprogram.com ). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number not applicable. Authors’ information All authors are affiliated with the Department of Statistics, Jagannath University, Dhaka-1100, Bangladesh. Funding The authors received no funding for this work. Author Contribution JH (Jakir Hossain) conceptualized the study, developed the main statistical analysis, drafted the manuscript, wrote the main script, and wrote the introduction. ASMRR (Abu Sayeed Md. Ripon Rouf) reviewed the manuscript critically. MT (Muhammad Tareq) checked and proofread the final document. MR (Md. Rokunuzzaman) handled the data processing and management. SKDS (Samrat Kumar Dev Sharma) reviewed the final document and refined this. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the Demographic and Health Survey (DHS) Program for providing access to the dataset. Data Availability The datasets used and/or analyzed during the current study are available from the DHS Program upon reasonable request and registration. Data access is granted at: [https://dhsprogram.com/data/available-datasets.cfm](https:/dhsprogram.com/data/available-datasets.cfm) . References Hossain MM, Abdulla F, Banik R, Yeasmin S, Rahman A. Child marriage and its association with morbidity and mortality of under-5 years old children in Bangladesh. PLoS ONE. Feb. 2022;17(2):e0262927. 10.1371/journal.pone.0262927 . Sagalova V, et al. Levels and trends of adolescent marriage and maternity in West and Central Africa, 1986–2017. J Glob Health. Aug. 2021;11:13001. 10.7189/jogh.11.13001 . Liang M, Simelane S, Chalasani S, Snow R. 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Public Health , vol. 2018, pp. 1–15, Jul. 2018. 10.1155/2018/7151297 Tampah-Naah AM, Osman A, Kumi-Kyereme A. Geospatial analysis of childhood morbidity in Ghana, PLOS ONE , vol. 14, no. 8, p. e0221324, Aug. 2019, 10.1371/journal.pone.0221324 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 09 Jan, 2026 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers invited by journal 11 Aug, 2025 Editor invited by journal 11 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 06 Aug, 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. 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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-7306650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498874497,"identity":"ccf847ab-c81c-4621-85a6-875eebcdf183","order_by":0,"name":"Jakir Hossain","email":"data:image/png;base64,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","orcid":"","institution":"Jagannath University","correspondingAuthor":true,"prefix":"","firstName":"Jakir","middleName":"","lastName":"Hossain","suffix":""},{"id":498874498,"identity":"03bf15e1-8c03-4ebc-adff-6e849a63b57f","order_by":1,"name":"Abu Sayeed Md. Ripon Rouf","email":"","orcid":"","institution":"Jagannath University","correspondingAuthor":false,"prefix":"","firstName":"Abu","middleName":"Sayeed Md.","lastName":"Ripon","suffix":"Md."},{"id":498874499,"identity":"4881c5fb-2180-4572-9ff8-ab7342600bed","order_by":2,"name":"Muhammad Tareq","email":"","orcid":"","institution":"Jagannath University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Tareq","suffix":""},{"id":498874500,"identity":"669597db-99a1-4cbc-89fc-c9b64444401b","order_by":3,"name":"Md. Rokunuzzaman","email":"","orcid":"","institution":"Jagannath University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Rokunuzzaman","suffix":""},{"id":498874501,"identity":"3ab60946-f29f-4487-9ffb-0e2a7fedc542","order_by":4,"name":"Samrat Kumar Dev Sharma","email":"","orcid":"","institution":"Jagannath University","correspondingAuthor":false,"prefix":"","firstName":"Samrat","middleName":"Kumar Dev","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-08-06 07:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7306650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7306650/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12887-026-06596-x","type":"published","date":"2026-02-10T15:58:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102785266,"identity":"c29d1c3e-6ff4-40ca-9789-59cc3ea4beeb","added_by":"auto","created_at":"2026-02-16 16:03:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1566914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7306650/v1/563dcd7a-ba24-4a12-b04d-7bfdaf6b7246.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Impact of Early Marriage and Socioeconomic Determinants on Under-Five Morbidity: A Cross-Country Analysis in South Asia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarly marriage, defined as marriage before the age of 18, remains a major social issue globally, particularly in developing countries. In low- and middle-income nations, it disproportionately impacts females and violates their rights. According to UNICEF, around 12\u0026nbsp;million girls are still married before turning 18 every year. The highest rates are found in South Asia (30%), Latin America (25%), and Sub-Saharan Africa (38%) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to data from 41 low-income nations, the global rate of early marriage is approximately 30%, with Niger having the highest rate at 75%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In India, 22% of women aged 20 to 24 gave birth before age 18, and 44.5% were married before 18. Early motherhood is linked to higher risks of stillbirth, neonatal death, low birth weight, and illness [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. By 2007, 74.9% of Pakistani women aged 20 to 24 had at least one child, with 31.6% giving birth in the first year of their marriage, and 50% being married before turning 18[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In 2018, over one-third of women in Pakistan and Afghanistan were married before turning 18, influenced by conservative Islamic cultural norms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In Nepal, 33% of women aged 20\u0026ndash;24 married before age 18, and 8% before 15 years, even though the legal minimum age is 20 years old, or 18 years with parental agreement[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Child marriage damages both mothers and their children under the age of five, raising the risk of morbidity. In 2017, over 15,000 children under the age of five died every day worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These risks are associated with adolescent vulnerability, such as maternal sadness, poverty, and hunger. Antenatal care, skillful delivery, and child vaccines are all less common due to limited education and healthcare availability[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Young married girls are also at risk because of their shorter birth spacing and higher risk of HIV infection[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To determine the influence of early marriage on infant morbidity, it is critical to distinguish between health hazards associated with early childbirth and the socioeconomic determinants and structural vulnerabilities that young mothers experience[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous social, demographic, and environmental factors affect children's nutrition and anemia; among these, the mother's age at marriage is a significant predictor of birth outcomes, nutrition, and cognitive development[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Respiratory infections and diarrhea are most common in the first two years, affecting growth and increasing child mortality.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Approximately 40\u0026ndash;60% of children worldwide receive proper care for diarrhea and ARIs, which remain top killers of children under five[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The prevalence of common illnesses, including AIDS, fever, cough, diarrhea, and malaria, keeps childhood morbidity high[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Understanding infection origins and transmission is critical for effective control.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Child health is strongly influenced by socioeconomic factors such as parental education, wealth, residence, healthcare access, and maternal nutrition[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Effective approaches include teaching and supporting girls, boosting female education quality, providing financial incentives, and engaging parents and community leaders to discourage early marriage[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Girls who stay in school longer have a lower chance of getting pregnant[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Improving child health and empowering females, while addressing socioeconomic issues, is critical in low-income countries for reducing early marriage and boosting survival and growth.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The extant literature highlights the noteworthy influence of early marriage on the rates of morbidity among children in South Asia. Although early marriage and its negative effects on child health have been examined, there remains a significant gap in understanding how early marriage, combined with various socioeconomic factors, specifically affects under-five morbidity in South Asia. This work fills a gap by investigating the combined impact of early marriage and socioeconomic variables on under-five morbidity using an analysis of the DHS child dataset from South Asia.\u003c/p\u003e"},{"header":"Objectives of the study","content":"\u003cp\u003eThe intention of this study is to find out the impact of early maternal marriage and socioeconomic factors on sickness among South Asian children.\u003c/p\u003e\u003cp\u003eThe specific objectives are: To assess the prevalence of child illness in several South Asian nations. To mark out the impact of notable socioeconomic factors on morbidity in children. To analyze the effect of early marriage and socioeconomic determinants on child morbidity using statistical models.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eWe used the analysis based on secondary data. These surveys employed a two-stage stratified sampling technique. This study utilized child recode datasets from recent Demographic and Health Surveys in five South Asian countries, focusing exclusively on children under the age of five. In Bangladesh, data from the 2017\u0026ndash;18 BDHS included 8,772 children[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], in India, the 2019\u0026ndash;21 NFHS covered 230,870 children[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], in Pakistan, the 2017\u0026ndash;18 PDHS included 10,494 children[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], in Afghanistan, the 2015 AfDHS surveyed 31,802 children[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and in Nepal, the 2022 NDHS involved 5,193 children[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The present study only included children under the age of five from these nationally representative household surveys.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTarget Variable\u003c/h3\u003e\n\u003cp\u003eThe study's main goal variable is the morbidity indicators of children under five. In the two weeks before the survey, the three variables that comprised the morbidity indicators were fever, diarrhea, and acute respiratory infections (ARIs). Thus, all outcome variables were recoded as yes\u0026thinsp;=\u0026thinsp;1 and no\u0026thinsp;=\u0026thinsp;0 to do the analysis.\u003c/p\u003e\n\u003ch3\u003eMorbidity Indicators\u003c/h3\u003e\n\u003cp\u003eDiarrhea in the last two weeks: This was evaluated by asking the kid if they had experienced diarrhea in the previous two weeks, with \"1\" coding \"yes\" and \"0\" coding \"no\". Fever within the previous two weeks: This was evaluated by asking if the kid had a fever during that time, with \"1\" coding \"yes\" and \"0\" coding \"no\". Two questions measuring whether the kid had experienced a cough and rapid breathing in the previous two weeks were used to determine whether the child had an acute respiratory infection (ARI), with \"1\" coding \"yes\" and \"0\" coding \"no\". And then we recoded the morbidity variable with \"1\" coding \"yes\" and \"0\" coding \"no\".\u003c/p\u003e\n\u003ch3\u003eIndependent Variables\u003c/h3\u003e\n\u003cp\u003eThe main predictor variable in this study is early marriage, which is defined as an adult marriage when the respondent was 18 years of age or older and an early marriage when the respondent was under 18. Additionally, several socioeconomic and demographic factors are incorporated into the study to examine their relationship to early marriage, and they serve as controlled variables in the adjusted logistic regression model. There are several independent variables such as age at first marriage, child sex, multiple births, birth order, maternal age, maternal education, paternal education, mass media exposure, number of ANC visits, delivery by caesarean section, place of delivery, maternal BMI, sex of household head, wealth index, and place of residence.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe impact of early marriage and socioeconomic determinants on children's morbidity was analyzed through descriptive statistics and binary logistic regression methods to assess potential risks. The Pearson chi-square test was used to show the association between categorical variables. Binary logistic regression model has also been utilized to assess the impact of early marriage and socioeconomic determinants on child morbidity in children under five. Relationships between factors were quantified by computing adjusted odds ratios with 95% confidence intervals. There were controlling variables in adjusted analyses related to child morbidity. Using Microsoft Word, Microsoft Excel, and SPSS Version 25, all data were weighted and examined.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBinary Logistic Regression Model\u003c/h3\u003e\n\u003cp\u003eBinary logistic regression is an appropriate statistical method for modeling the relationship between a dichotomous dependent variable and one or more independent variables, which may be continuous or categorical. In this study, binary logistic regression was employed to estimate the likelihood of the binary outcome based on a set of explanatory variables. The logistic regression model estimates the probability of occurrence of the event of interest by fitting data to a logistic function. The model is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left(\\frac{p}{1-p}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}\\:+\\dots\\:+{\\beta\\:}_{k}{X}_{k}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e is the probability of the event, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{2},\\dots\\:,{X}_{k}\\)\u003c/span\u003e\u003c/span\u003e are independent variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0},{\\beta\\:}_{1},{\\beta\\:}_{2},\\dots\\:,{\\beta\\:}_{k}\\:\\)\u003c/span\u003e\u003c/span\u003eare the model coefficients estimated through maximum likelihood estimation (MLE). The coefficients are interpreted in terms of odds ratios (OR), where \u003cem\u003eOR\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:={e}^{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e. An odds ratio greater than 1 indicates a positive association with the outcome, whereas an odds ratio less than 1 indicates a negative association.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePrevalence of child morbidity:\u003c/h2\u003e\u003cp\u003eShowing the child morbidity scenario among children aged under five years across five South Asian countries. Pakistan had the highest percentage, 44.5% of child morbidity in South Asia, while India had the lowest, 17.7%. Of the children from Afghanistan, 42.3% had child morbidity, followed by Bangladesh at 34.2%, and Nepal at 28.3% as shown in 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\u003ePrevalence of child morbidity in South Asian countries.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal individuals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo of illness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e230870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfghanistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNepal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAssociation between independent variables and morbidity outcome:\u003c/h2\u003e\u003cp\u003eThe findings of Pearson chi-square tests, which evaluated the bivariate relationships between early maternal marriage, important socioeconomic factors, and the rate of morbidity in children under five, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In South Asia, 35.2% of women in Bangladesh, 19.6% in India, and 43.3% in Afghanistan marry before the age of 18, demonstrating that early marriage is common throughout the region. In Bangladesh, 35.3% of male children and 32.9% of female children have morbidity, while in India, the rates are 18.3% for male children and 17.1% for female children. In India, 17.8% of children with morbidity are single births, whereas 13.2% are twins or higher-order multiples. In Pakistan, 44.8% of births are singletons, and 30.7% are multiples. In Afghanistan, 42.4% of births are singletons, and 34.8% are multiples. In Nepal, 28.5% of births are singletons, and 15.2% are multiples. Morbidity affects 17.5% of first-born infants in India, 42.8% of second- or third-born children in Afghanistan, and 29.6% of first-born children in Nepal. The prevalence of morbidity among breastfeeding children is 40.4% in Bangladesh, 21.8% in India, 54.0% in Pakistan, 48.8% in Afghanistan, and 31.4% in Nepal, while among non-breastfeeding children, the rates are 28.7%, 14.3%, 40.5%, 38.9%, and 25.3%, respectively. Morbidity affects 39.3% of children born to women aged 15\u0026ndash;18 years in Bangladesh, 25.4% in India, and 55.2% in Pakistan. Child morbidity rates differ by maternal education level: in Bangladesh, 31.6% for uneducated and 30.4% for secondary-educated mothers; in India, 16.7% for uneducated and 15.2% for higher-educated mothers; in Pakistan, 39.9% for uneducated and 51.5% for secondary-educated mothers; in Afghanistan, 46.1% for primary and 33.5% for higher-educated mothers; and in Nepal, 24.8% for uneducated and 30.4% for secondary-educated mothers. Children with uneducated fathers have a morbidity rate of 16.8% in India, 41.9% in Pakistan, and 43.0% in Afghanistan. 17.4% of mothers in India, 47.0% in Pakistan, and 29.7% in Nepal have access to mass media, which is critical for raising knowledge regarding child health and morbidity. The incidence of child morbidity among mothers with no ANC visits is 23.5% in India, 44.3% in Pakistan, and 40.7% in Afghanistan, but for those with 8 or more ANC visits, the rates are 19.5% in India, 58.1% in Pakistan, and 1.4% in Afghanistan. Morbidity among children born via cesarean section is 18.5% in India, 47.7% in Pakistan, and 49.0% in Afghanistan. In Pakistan, 41.7% of deliveries are normal, and 45.8% take place at health facilities. Child morbidity rates among mothers with an underweight BMI are 35.9% in Bangladesh and 19.7% in India, compared to 32.4% and 16.7% for mothers with an overweight or obese BMI, respectively. In India, child morbidity rates are 17.4% for male-headed households and 19.4% for female-headed households. Child morbidity rates are 36.0% in Bangladeshi middle-class households, 19.1% in Indian poor households, and 45.8% in Pakistani wealthy households. Child morbidity rates in urban areas of Bangladesh are 31.9%, 16.1% in urban India, 45.1% in urban Afghanistan, and 29.4% in urban Nepal.\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\u003eAssociation of early marriage and socioeconomic determinants with child morbidity: Bivariate Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eExplanatory\u003c/p\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u003cp\u003eChild morbidity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eAfghanistan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003eNepal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at first marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarly marriage\u003c/p\u003e\u003cp\u003eAdult marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2204(35.2)\u003c/p\u003e\u003cp\u003e796(31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4053(64.8) 1719(68.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15118(19.6) 25695(16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62157(80.4) 127501(83.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1567(43.9) 3097(44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2005(56.1) 3821(55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7095(43.3) 6297(41.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9290(56.7) 8996(58.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e684(27.4) 786(29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1810(72.6) 1911(70.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1620(35.3) 1380(32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2963(64.7) 2809(67.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21964(18.3) 18939(17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97995(81.7) 91971(82.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2355(44.4)\u003c/p\u003e\u003cp\u003e2310(44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2908(55.3)\u003c/p\u003e\u003cp\u003e2921(55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7016(42.7)\u003c/p\u003e\u003cp\u003e6421(41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9398(573)\u003c/p\u003e\u003cp\u003e8966(58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e800(29.3)\u003c/p\u003e\u003cp\u003e671(27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1929(70.7)\u003c/p\u003e\u003cp\u003e1793(72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild twin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle birth\u003c/p\u003e\u003cp\u003eMultiple births\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2950(34.3) 50(30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5660(65.7) 112(69.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40372(17.8) 531(13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e186486(82.2) 3481(86.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4587(44.8)\u003c/p\u003e\u003cp\u003e78(30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5653(55.2)\u003c/p\u003e\u003cp\u003e176(69.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13251(42.4)\u003c/p\u003e\u003cp\u003e187(34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e18014(57.6)\u003c/p\u003e\u003cp\u003e350(65.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1461(28.5)\u003c/p\u003e\u003cp\u003e10(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e3666(71.5)\u003c/p\u003e\u003cp\u003e56(84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth order\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst\u003c/p\u003e\u003cp\u003eSecond or third\u003c/p\u003e\u003cp\u003eFourth and up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1112(32.8) 1495(35.0)\u003c/p\u003e\u003cp\u003e393(35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2283(67.2) 2779(65.0)\u003c/p\u003e\u003cp\u003e710(64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15803(17.5)\u003c/p\u003e\u003cp\u003e19994(17.7)\u003c/p\u003e\u003cp\u003e5101(18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74524(82.5)\u003c/p\u003e\u003cp\u003e92916(82.3)\u003c/p\u003e\u003cp\u003e22498(81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1157(45.4)\u003c/p\u003e\u003cp\u003e1801(44.5)\u003c/p\u003e\u003cp\u003e1693(43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1391(54.6)\u003c/p\u003e\u003cp\u003e2250(55.5)\u003c/p\u003e\u003cp\u003e2174(56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2449(40.4)\u003c/p\u003e\u003cp\u003e4440(42.8)\u003c/p\u003e\u003cp\u003e6455(42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3619(59.6)\u003c/p\u003e\u003cp\u003e5924(57.2)\u003c/p\u003e\u003cp\u003e8736(57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e605(28.5)\u003c/p\u003e\u003cp\u003e736(29.6)\u003c/p\u003e\u003cp\u003e130(22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1518(71.5)\u003c/p\u003e\u003cp\u003e1749(70.4)\u003c/p\u003e\u003cp\u003e454(77.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently breastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1328(28.7) 1672(40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3307(71.3) 2466(59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18082(14.3) 22822(21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e108153(85.7) 81814(78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2993(40.5) 1672(54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4403(59.5) 1426(46.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8213(38.9) 5225(48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e12885(61.1) 5479(51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e658(25.3) 813(31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1946(74.7) 1776(68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother's age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;18\u003c/p\u003e\u003cp\u003e19\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252(39.3) 2748(33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e389(60.7) 5383(66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e610(25.4) 40293(17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1793(74.6) 188173(82.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e74(55.2)\u003c/p\u003e\u003cp\u003e4591(44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e60(44.8)\u003c/p\u003e\u003cp\u003e5769(55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e241(43.1)\u003c/p\u003e\u003cp\u003e13196(42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e318(56.9)\u003c/p\u003e\u003cp\u003e18046(57.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e46(31.1)\u003c/p\u003e\u003cp\u003e1425(28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e102(68.9)\u003c/p\u003e\u003cp\u003e3620(71.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204(31.6)\u003c/p\u003e\u003cp\u003e866(34.3)\u003c/p\u003e\u003cp\u003e1518(35.7)\u003c/p\u003e\u003cp\u003e411(30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e441(68.4)\u003c/p\u003e\u003cp\u003e1661(65.7)\u003c/p\u003e\u003cp\u003e2731(64.3)\u003c/p\u003e\u003cp\u003e939(69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8255(16.7)\u003c/p\u003e\u003cp\u003e5212(18.3) 21938(18.7)\u003c/p\u003e\u003cp\u003e5498(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41051(83.3)\u003c/p\u003e\u003cp\u003e32222(81.7) 95093(81.3)\u003c/p\u003e\u003cp\u003e30601(84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2065(39.9)\u003c/p\u003e\u003cp\u003e832(47.7)\u003c/p\u003e\u003cp\u003e1160(51.5)\u003c/p\u003e\u003cp\u003e607(46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3113(60.1)\u003c/p\u003e\u003cp\u003e913(52.3)\u003c/p\u003e\u003cp\u003e1091(48.5)\u003c/p\u003e\u003cp\u003e711(53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11168(42.0)\u003c/p\u003e\u003cp\u003e1154(46.1)\u003c/p\u003e\u003cp\u003e952(42.5)\u003c/p\u003e\u003cp\u003e164(33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e15399(58.0)\u003c/p\u003e\u003cp\u003e1350(53.9)\u003c/p\u003e\u003cp\u003e1289(57.5)\u003c/p\u003e\u003cp\u003e326(66.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e286(24.8)\u003c/p\u003e\u003cp\u003e517(28.3)\u003c/p\u003e\u003cp\u003e606(30.4)\u003c/p\u003e\u003cp\u003e62(28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e866(75.2)\u003c/p\u003e\u003cp\u003e1313(71.7)\u003c/p\u003e\u003cp\u003e1389(69.6)\u003c/p\u003e\u003cp\u003e153(71.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFather\u0026rsquo;s education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eSecondary and Higher\u003c/p\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e435(33.4)\u003c/p\u003e\u003cp\u003e1026(34.7)\u003c/p\u003e\u003cp\u003e1485(34.2)\u003c/p\u003e\u003cp\u003e4(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e866(66.6)\u003c/p\u003e\u003cp\u003e1935(65.3)\u003c/p\u003e\u003cp\u003e2854(65.8)\u003c/p\u003e\u003cp\u003e24(85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e892(16.8)\u003c/p\u003e\u003cp\u003e905(19.4)\u003c/p\u003e\u003cp\u003e4553(18.2)\u003c/p\u003e\u003cp\u003e35(22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4429(83.2)\u003c/p\u003e\u003cp\u003e3750(80.6)\u003c/p\u003e\u003cp\u003e20480(81.8)\u003c/p\u003e\u003cp\u003e120(77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1283(41.9)\u003c/p\u003e\u003cp\u003e810(45.2)\u003c/p\u003e\u003cp\u003e2491(45.6)\u003c/p\u003e\u003cp\u003e6(21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1778(58.1)\u003c/p\u003e\u003cp\u003e983(54.8)\u003c/p\u003e\u003cp\u003e2967(54.4)\u003c/p\u003e\u003cp\u003e22(78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7823(42.7)\u003c/p\u003e\u003cp\u003e1981(43.0)\u003c/p\u003e\u003cp\u003e3510(41.0)\u003c/p\u003e\u003cp\u003e93(37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10477(57.3)\u003c/p\u003e\u003cp\u003e2625(57.0)\u003c/p\u003e\u003cp\u003e5042(59.0)\u003c/p\u003e\u003cp\u003e157(62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e143(24.4)\u003c/p\u003e\u003cp\u003e609(29.8)\u003c/p\u003e\u003cp\u003e677(28.1)\u003c/p\u003e\u003cp\u003e30(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e442(75.6)\u003c/p\u003e\u003cp\u003e1433(70.2)\u003c/p\u003e\u003cp\u003e1735(71.9)\u003c/p\u003e\u003cp\u003e70(70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMass media exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Exposed\u003c/p\u003e\u003cp\u003eExposed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1384(34.8)\u003c/p\u003e\u003cp\u003e1616(33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2598(65.2)\u003c/p\u003e\u003cp\u003e3175(66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20755(18.0)\u003c/p\u003e\u003cp\u003e20149(17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94384(82.0)\u003c/p\u003e\u003cp\u003e95583(82.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2261(42.1)\u003c/p\u003e\u003cp\u003e2404(47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3114(57.9)\u003c/p\u003e\u003cp\u003e2716(53.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6405(42.2)\u003c/p\u003e\u003cp\u003e7032(42.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8770(57.8)\u003c/p\u003e\u003cp\u003e9594(57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e776(27.2)\u003c/p\u003e\u003cp\u003e695(29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e2079(72.8)\u003c/p\u003e\u003cp\u003e1643(70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo ANC\u003c/p\u003e\u003cp\u003eLess than 8\u003c/p\u003e\u003cp\u003e8+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168(41.5)\u003c/p\u003e\u003cp\u003e1599(39.1)\u003c/p\u003e\u003cp\u003e197(35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e237(58.5)\u003c/p\u003e\u003cp\u003e2488(60.9)\u003c/p\u003e\u003cp\u003e361(64.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3051(23.5)\u003c/p\u003e\u003cp\u003e25148(19.6)\u003c/p\u003e\u003cp\u003e6563(19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9905(76.5)\u003c/p\u003e\u003cp\u003e103185(80.4)\u003c/p\u003e\u003cp\u003e27095(80.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e374(44.3)\u003c/p\u003e\u003cp\u003e2504(50.8)\u003c/p\u003e\u003cp\u003e544(58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e470(55.7)\u003c/p\u003e\u003cp\u003e2425(49.2)\u003c/p\u003e\u003cp\u003e393(41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3199(40.7)\u003c/p\u003e\u003cp\u003e5954(52.7)\u003c/p\u003e\u003cp\u003e270(57.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e4655(59.3)\u003c/p\u003e\u003cp\u003e5353(47.3)\u003c/p\u003e\u003cp\u003e201(42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e20(26.9)\u003c/p\u003e\u003cp\u003e814(31.3)\u003c/p\u003e\u003cp\u003e56(33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e55(73.3)\u003c/p\u003e\u003cp\u003e1789(68.7)\u003c/p\u003e\u003cp\u003e110(66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelivery by caesarean section\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1378(38.4)\u003c/p\u003e\u003cp\u003e660(37.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2206(61.6)\u003c/p\u003e\u003cp\u003e1087(62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31715(17.5) 9188(18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e149521(82.5) 40446(81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3539(43.5) 1116(47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4604(56.5) 1223(52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13000(42.1) 422(49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e17864(57.9) 439(51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e735(29.4) 190(33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1764(70.6) 382(66.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespondents Home\u003c/p\u003e\u003cp\u003eWith Health Facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1051(39.4) 988(37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1619(60.6) 1679(63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4647(18.0)\u003c/p\u003e\u003cp\u003e36256(17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21201(82.0)\u003c/p\u003e\u003cp\u003e168765(82.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1475(41.7)\u003c/p\u003e\u003cp\u003e3190(45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2058(58.3)\u003c/p\u003e\u003cp\u003e3768(54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6917(42.7)\u003c/p\u003e\u003cp\u003e6515(42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9271(57.3)\u003c/p\u003e\u003cp\u003e8966(57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e166(27.3)\u003c/p\u003e\u003cp\u003e758(30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e443(72.7)\u003c/p\u003e\u003cp\u003e1704(69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003cp\u003eNormal\u003c/p\u003e\u003cp\u003eOverweight/obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e421(35.9)\u003c/p\u003e\u003cp\u003e1734(35.5)\u003c/p\u003e\u003cp\u003e698(32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e753(64.1)\u003c/p\u003e\u003cp\u003e3152(64.5)\u003c/p\u003e\u003cp\u003e1456(67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8161(19.7)\u003c/p\u003e\u003cp\u003e22028(17.6)\u003c/p\u003e\u003cp\u003e6769(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33361(80.3)\u003c/p\u003e\u003cp\u003e103267(82.4)\u003c/p\u003e\u003cp\u003e33844(83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e182(44.6)\u003c/p\u003e\u003cp\u003e723(48.3)\u003c/p\u003e\u003cp\u003e688(46.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e226(55.4)\u003c/p\u003e\u003cp\u003e774(51.7)\u003c/p\u003e\u003cp\u003e797(53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e94(27.2)\u003c/p\u003e\u003cp\u003e443(29.2)\u003c/p\u003e\u003cp\u003e208(32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e251(72.8)\u003c/p\u003e\u003cp\u003e1076(70.8)\u003c/p\u003e\u003cp\u003e431(67.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of household head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2582(34.0)\u003c/p\u003e\u003cp\u003e418(35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5017(66.0)\u003c/p\u003e\u003cp\u003e755(64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34098(17.4)\u003c/p\u003e\u003cp\u003e6805(19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e161730(82.6)\u003c/p\u003e\u003cp\u003e28235(80.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4130(44.2)\u003c/p\u003e\u003cp\u003e535(46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5204(55.8)\u003c/p\u003e\u003cp\u003e626(53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13279(42.2) 158(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e18168(57.8) 196(55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1013(28.6)\u003c/p\u003e\u003cp\u003e457(27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e2535(71.4)\u003c/p\u003e\u003cp\u003e1187(72.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1276(34.8)\u003c/p\u003e\u003cp\u003e593(36.0)\u003c/p\u003e\u003cp\u003e1131(32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2388(65.2)\u003c/p\u003e\u003cp\u003e1053(64.0)\u003c/p\u003e\u003cp\u003e2331(67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20388(19.1)\u003c/p\u003e\u003cp\u003e8233(18.3)\u003c/p\u003e\u003cp\u003e12282(15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86552(80.9)\u003c/p\u003e\u003cp\u003e36868(81.7)\u003c/p\u003e\u003cp\u003e66546(84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1899(42.3)\u003c/p\u003e\u003cp\u003e1011(46.4)\u003c/p\u003e\u003cp\u003e1755(45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2587(57.7)\u003c/p\u003e\u003cp\u003e1167(53.6)\u003c/p\u003e\u003cp\u003e2076(54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5289(41.9)\u003c/p\u003e\u003cp\u003e2886(42.6)\u003c/p\u003e\u003cp\u003e5262(42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7343(58.1)\u003c/p\u003e\u003cp\u003e3892(57.4)\u003c/p\u003e\u003cp\u003e7129(57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e646(27.5)\u003c/p\u003e\u003cp\u003e332(30.8)\u003c/p\u003e\u003cp\u003e493(27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1700(72.5)\u003c/p\u003e\u003cp\u003e747(69.2)\u003c/p\u003e\u003cp\u003e1274(72.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e769(31.9)\u003c/p\u003e\u003cp\u003e2231(35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1644(68.1)\u003c/p\u003e\u003cp\u003e4129(64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9922(16.1)\u003c/p\u003e\u003cp\u003e30981(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51606(83.9)\u003c/p\u003e\u003cp\u003e138361(81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1533(45.7)\u003c/p\u003e\u003cp\u003e3132(43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1819(54.3)\u003c/p\u003e\u003cp\u003e4011(56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3269(45.1)\u003c/p\u003e\u003cp\u003e10168(41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3977(54.9)\u003c/p\u003e\u003cp\u003e14387(58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e991(29.4)\u003c/p\u003e\u003cp\u003e480(26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e2376(70.6)\u003c/p\u003e\u003cp\u003e1346(73.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.016\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\u003eNote: Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the bivariate associations between early maternal marriage, socioeconomic and demographic characteristics, and morbidity among children under the age of five. Pearson's Chi-squared test was used to analyze the connections. A p-value of less than 0.05 indicates statistical significance.\u003c/p\u003e\u003cp\u003eN/A\u0026thinsp;=\u0026thinsp;Not Available\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSignificant risk factors for under-five child morbidity across South Asian countries:\u003c/h2\u003e\u003cp\u003eTo better understand the determinants of under-five morbidity, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the adjusted odds ratios (OR) derived from binary logistic regression models across five South Asian countries. The study finds that various child, mother, and household-level characteristics are strongly linked with the risk of morbidity. The following is a descriptive overview that focuses on the factors that exhibited a statistically significant positive association with child morbidity, provided by the nation. Breastfeeding was the only significant risk factor in Bangladesh, with children being 29% more likely to suffer morbidity (AOR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.09\u0026ndash;1.52), probably due to reverse causation. In India, morbidity was significantly higher among breastfed children (AOR\u0026thinsp;=\u0026thinsp;1.32, 95% CI: 1.24\u0026ndash;1.42), children of mothers with secondary education (AOR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.01\u0026ndash;1.24), fathers with secondary/higher education (AOR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.04\u0026ndash;1.29), those exposed to mass media (AOR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.01\u0026ndash;1.16), cesarean births (AOR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.01\u0026ndash;1.18), and children from female-headed households (AOR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.05\u0026ndash;1.24), and Adult marriage decreased the chance of morbidity (AOR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.77\u0026ndash;0.88), as did maternal higher education (AOR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.70\u0026ndash;0.93), rural residency (AOR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.80\u0026ndash;0.94), and normal maternal BMI. In Pakistan, female children were more likely to have morbidity (AOR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.20\u0026ndash;1.70), whereas maternal secondary education (AOR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.40\u0026ndash;2.53) and eight or more antenatal care (ANC) visits (AOR\u0026thinsp;=\u0026thinsp;1.75, 95% CI: 1.17\u0026ndash;2.63) were also linked with increased risk. In Afghanistan, breastfed children (AOR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.01\u0026ndash;1.13), children of older mothers (19\u0026ndash;49 years) (AOR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.01\u0026ndash;1.49), and those whose mothers had 8 or more ANC visits (AOR\u0026thinsp;=\u0026thinsp;2.23, 95% CI: 1.83\u0026ndash;2.71) had significantly higher morbidity rates, and protective factors against child morbidity included adult marriage (AOR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.82\u0026ndash;0.92), multiple births (AOR\u0026thinsp;=\u0026thinsp;0.62, 95% CI: 0.47\u0026ndash;0.83), maternal higher education (AOR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.45\u0026ndash;0.72), delivery at a health facility (AOR\u0026thinsp;=\u0026thinsp;0.78, 95% CI: 0.73\u0026ndash;0.83), and rural residence (AOR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.73\u0026ndash;0.87). In Nepal, none of the variables investigated had statistically significant relationships with under-five morbidity.\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\u003eCountry-wise binary logistic regression estimates of factors associated with under-five morbidity in South Asia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eExplanatory\u003c/p\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u003cp\u003eChild morbidity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBangladesh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eIndia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003ePakistan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e\u003cb\u003eAfghanistan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003eNepal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eORs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eORs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eORs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eORs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eORs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eupper-lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eupper-lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eupper-lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eupper-lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eupper-lower\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at first marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarly marriage\u003c/p\u003e\u003cp\u003eAdult marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.78\u0026ndash;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.82***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.77\u0026ndash;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.67\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.87***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.82\u0026ndash;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.76\u0026ndash;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.81\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.92*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.87\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.43***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.20\u0026ndash;1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.92\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.80\u0026ndash;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild twin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle birth\u003c/p\u003e\u003cp\u003eMultiple births\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.62-2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.59\u0026ndash;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.29\u0026ndash;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.62***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.47\u0026ndash;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.13\u0026ndash;3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth order\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst\u003c/p\u003e\u003cp\u003eSecond or third\u003c/p\u003e\u003cp\u003eFourth and up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.08\u003c/p\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.94\u0026ndash;1.25\u003c/p\u003e\u003cp\u003e0.93\u0026ndash;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.90**\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.76***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.84\u0026ndash;0.97\u003c/p\u003e\u003cp\u003e0.68\u0026ndash;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.14\u003c/p\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.88\u0026ndash;1.47\u003c/p\u003e\u003cp\u003e0.84\u0026ndash;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.88\u0026ndash;1.06\u003c/p\u003e\u003cp\u003e0.85\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.15\u003c/p\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.88\u0026ndash;1.50\u003c/p\u003e\u003cp\u003e0.52\u0026ndash;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently breastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1\u003cb\u003e.29***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.09\u0026ndash;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.32***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.24\u0026ndash;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.59***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.33\u0026ndash;1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.07*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.01\u0026ndash;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.90\u0026ndash;2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother's age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;18\u003c/p\u003e\u003cp\u003e19\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.77\u0026ndash;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.76\u0026ndash;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.18*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.09\u0026ndash;4.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.22*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.01\u0026ndash;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.45\u0026ndash;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e0.91\u003c/p\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.66\u0026ndash;1.12\u003c/p\u003e\u003cp\u003e0.69\u0026ndash;1.19\u003c/p\u003e\u003cp\u003e0.57\u0026ndash;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003cp\u003e0.471\u003c/p\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.08\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.12*\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.81***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.96\u0026ndash;1.22\u003c/p\u003e\u003cp\u003e1.01\u0026ndash;1.24\u003c/p\u003e\u003cp\u003e0.70\u0026ndash;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003cp\u003e0.029\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.37*\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.88***\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.03\u0026ndash;1.82\u003c/p\u003e\u003cp\u003e1.40\u0026ndash;2.53\u003c/p\u003e\u003cp\u003e0.95\u0026ndash;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.15*\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.57***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.03\u0026ndash;1.28\u003c/p\u003e\u003cp\u003e0.86\u0026ndash;1.09\u003c/p\u003e\u003cp\u003e0.45\u0026ndash;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.937\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.03\u003c/p\u003e\u003cp\u003e1.20\u003c/p\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.70\u0026ndash;1.52\u003c/p\u003e\u003cp\u003e0.78\u0026ndash;1.87\u003c/p\u003e\u003cp\u003e0.95\u0026ndash;3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003cp\u003e0.407\u003c/p\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFather\u0026rsquo;s education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eSecondary and Higher\u003c/p\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.04\u003c/p\u003e\u003cp\u003e1.04\u003c/p\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.86\u0026ndash;1.27\u003c/p\u003e\u003cp\u003e0.84\u0026ndash;1.27\u003c/p\u003e\u003cp\u003e0.06\u0026ndash;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003cp\u003e0.744\u003c/p\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.07\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.16**\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.94\u0026ndash;1.21\u003c/p\u003e\u003cp\u003e1.04\u0026ndash;1.29\u003c/p\u003e\u003cp\u003e0.85\u0026ndash;2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.93\u003c/p\u003e\u003cp\u003e0.80\u003c/p\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.70\u0026ndash;1.23\u003c/p\u003e\u003cp\u003e0.63\u0026ndash;1.03\u003c/p\u003e\u003cp\u003e0.16\u0026ndash;9.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003cp\u003e0.086\u003c/p\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.90\u0026ndash;1.07\u003c/p\u003e\u003cp\u003e0.91\u0026ndash;1.05\u003c/p\u003e\u003cp\u003e0.55\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003cp\u003e0.479\u003c/p\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.90\u003c/p\u003e\u003cp\u003e1.01\u003c/p\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.55\u0026ndash;1.42\u003c/p\u003e\u003cp\u003e0.61\u0026ndash;1.67\u003c/p\u003e\u003cp\u003e0.52\u0026ndash;3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003cp\u003e0.983\u003c/p\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMass media exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Exposed\u003c/p\u003e\u003cp\u003eExposed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.93\u0026ndash;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.08*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.01\u0026ndash;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.97\u0026ndash;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.93\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.70\u0026ndash;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo ANC\u003c/p\u003e\u003cp\u003eLess than 8\u003c/p\u003e\u003cp\u003e8+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.78\u0026ndash;1.23\u003c/p\u003e\u003cp\u003e0.66\u0026ndash;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.85**\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.84*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.75\u0026ndash;0.96\u003c/p\u003e\u003cp\u003e0.73\u0026ndash;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.51**\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.75**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.12\u0026ndash;2.05\u003c/p\u003e\u003cp\u003e1.17\u0026ndash;2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.73***\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.23***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.63\u0026ndash;1.85\u003c/p\u003e\u003cp\u003e1.83\u0026ndash;2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.13\u003c/p\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.51\u0026ndash;2.49\u003c/p\u003e\u003cp\u003e0.51\u0026ndash;3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelivery by caesarean section\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.91\u0026ndash;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.09*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.01\u0026ndash;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.69\u0026ndash;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.99\u0026ndash;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.79\u0026ndash;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespondents Home\u003c/p\u003e\u003cp\u003eWith Health Facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.79\u0026ndash;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.86\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.72**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.57\u0026ndash;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.78***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.73\u0026ndash;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.76\u0026ndash;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003cp\u003eNormal\u003c/p\u003e\u003cp\u003eOverweight/obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.92\u003c/p\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.78\u0026ndash;1.08\u003c/p\u003e\u003cp\u003e0.68\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.86***\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.87**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.80\u0026ndash;0.93\u003c/p\u003e\u003cp\u003e0.78\u0026ndash;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.16\u003c/p\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.86\u0026ndash;1.56\u003c/p\u003e\u003cp\u003e0.74\u0026ndash;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.20\u003c/p\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.85\u0026ndash;1.69\u003c/p\u003e\u003cp\u003e0.64\u0026ndash;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of household head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.84\u0026ndash;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.14***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.05\u0026ndash;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.71\u0026ndash;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.81\u0026ndash;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.67\u0026ndash;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.05\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.88\u0026ndash;1.25\u003c/p\u003e\u003cp\u003e0.81\u0026ndash;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.89*\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.79***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.82\u0026ndash;0.98\u003c/p\u003e\u003cp\u003e0.72\u0026ndash;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.75\u0026ndash;1.28\u003c/p\u003e\u003cp\u003e0.62\u0026ndash;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.94\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.87\u0026ndash;1.01\u003c/p\u003e\u003cp\u003e0.90\u0026ndash;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.95\u003c/p\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.69\u0026ndash;1.31\u003c/p\u003e\u003cp\u003e0.66\u0026ndash;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.93\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.95\u0026ndash;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.77\u0026ndash;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.80***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.73\u0026ndash;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e0.75\u0026ndash;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.803\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\u003eNote: * Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) are displayed. Each model is stratified by nation and includes predictors at the child, mother, and household levels. The table indicates reference categories. AORs with *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 are statistically significant. 1\u0026thinsp;=\u0026thinsp;Reference category, and N/A\u0026thinsp;=\u0026thinsp;Not Available.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study conducts a robust, cross-country analysis of how early marriage and socioeconomic determinants affect under-five child morbidity in five South Asian countries: Bangladesh, India, Pakistan, Afghanistan, and Nepal. We used coordinated, nationally representative DHS data and binary logistic regression to identify critical child, maternal, and household-level factors that are significantly associated with recent childhood morbidity. The study assessed the association between socio-demographic characteristics and the outcome of child health[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Early maternal marriage (\u0026lt;\u0026thinsp;18 years) is substantially linked with increased under-five morbidity in India and Afghanistan. However, the direction of the association shows decreased odds. This could be due to confounding or reporting biases, as previous research has demonstrated that early marriage affects maternal autonomy and access to child health services[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The early married child was significantly associated with an increased likelihood of under-five child morbidity than the adult married child[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In India, female children had reduced odds of morbidity, but in Pakistan, female children had greater odds, indicating that sex-based disparities in treatment or disease vulnerability differ by environment. It explicitly differentiates morbidity rates between boys and girls[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, multiple births were linked with significantly decreased morbidity in Afghanistan. Multiple births are substantially linked to greater rates of low birth weight and preterm birth, two factors that considerably exacerbate these children's morbidity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e],[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Birth order appears to have a strong impact on the parental treatment of child morbidity, especially when second- and third-born born are compared by child morbidity. Based on birth order, parental health investments varied significantly[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e],[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Interestingly, children who were currently breastfeeding had significantly increased risks of morbidity in all countries, including India and Pakistan. This paradoxical result may represent reverse causality: women breastfeed longer in reaction to child illness[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Breastfeeding, especially exclusive breastfeeding, is linked to infant morbidity and infection risks[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e],[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Maternal age yielded conflicting results. In Pakistan, older women (19\u0026ndash;49) were substantially more likely to report child morbidity, whereas in Afghanistan, the increase was small. These findings may reflect aging-related differences in birth spacing, health literacy, or maternal weariness. Other research found similar connections[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Maternal education was significantly beneficial in India and Afghanistan, according to global evidence that educated mothers are more likely to participate in effective health-seeking and disease preventive behaviors[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, paternal education was only substantially linked with morbidity in India, which could be attributed to better health awareness and reporting. This emphasizes how crucial elements like access to healthcare are[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, depending on additional variables like geographic location and economic standing, the effect of schooling on child morbidity can differ[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e],[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The habit of child feeding may benefit from the presence of educated parents[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In India, mass media exposure was related to increased morbidity, which may indicate that media-literate mothers are more likely to notice and report disease signs[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e],[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Antenatal care (ANC) had a wide range of effects. In India, \u0026ge;\u0026thinsp;8 ANC visits were protective, whereas in Pakistan and Afghanistan, more ANC was strangely related with increased morbidity. This may reflect that women with unwell children were more likely to obtain ANC, or that the quality of ANC treatment is inadequate in some circumstances[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The best maternal and fetal outcomes are associated with antenatal care (ANC)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e],[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Institutional delivery significantly reduced morbidity in Pakistan and Afghanistan, highlighting the importance of professional medical care at birth in mitigating early childhood disease risks. The immune system may develop differently in babies born with CS, increasing the risk of allergies, asthma, and a less diverse gut microbiota[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e],[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Children of mothers who were born in Health facilities were less likely to have child morbidity[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Maternal BMI (normal range) was related to lower morbidity only in India, which may reflect the country's nutrition transition and expanding urban-rural dietary disparities[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Underweight mothers have a higher chance of having low birth weight and small-for-gestational-age babies, which are connected to higher morbidity[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e],[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Premature birth, newborn asphyxia, fetal overgrowth, and increased neonatal morbidity are among the negative outcomes that are linked to higher maternal BMI[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e],[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Additionally, from households where the household head was female were more likely to have child morbidity[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Wealth index likewise had a high protective impact in India, but the link was modest or nonsignificant in Bangladesh, Pakistan, and Nepal. This emphasizes the importance of taking into account country-specific health financing and access limits when understanding the impact on health outcomes[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Finally, rural living was unexpectedly protective in Afghanistan, which could be attributed to reduced disease reporting, less environmental exposure, or changes in health-seeking behaviors. There was no consistent correlation in the other countries[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e],[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Taken together, our findings demonstrate that early marriage is a significant contextual predictor of under-five morbidity, although its effects are frequently moderated or changed by maternal education, healthcare utilization, and household wealth. The study emphasizes the need for multisectoral measures, such as health system strengthening, education promotion, and gender equity reforms, in improving child health outcomes in South Asia.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis study has various advantages, including a comprehensive cross-country analysis employing nationally representative data, which improves the generalizability and validity of conclusions using robust statistical approaches. It contributes to the current literature by emphasizing the importance of maternal education and prenatal care in child health. However, drawbacks include the use of cross-sectional data, which restricts causal inference, potential self-reported answer bias, cross-country data collection errors, and unmeasured confounders. Future studies should look into longitudinal designs and cultural and policy factors on child morbidity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study looked at the effects of early marriage and socioeconomic determinants on under-five illness in South Asia, and it found strong links between early maternal marriage, low education, low socioeconomic level, and increased child morbidity. The findings emphasize the significance of maternal education, delayed marriage, and improved access to prenatal and healthcare services in lowering child health disparities. Despite constraints such as cross-country data inconsistency, the study provides useful insights for policymakers. Future studies should investigate region-specific trends and community-based measures to mitigate the effects of early marriage on child health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDemographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBDHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBangladesh Demographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNFHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Family Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePDHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePakistan Demographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAfDHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAfghanistan Demographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNDHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNepal Demographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcquired Immunodeficiency Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute Respiratory Infection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAntenatal Care\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMaximum Likelihood Estimation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003cp\u003eThis study's foundation examined publicly accessible survey data sets stripped of all respondent-identifying information. The relevant Ethics Committees in Bangladesh, India, Pakistan, Afghanistan, Nepal, and ICF Macro at Calverton, USA, approved this survey. The Demographic and Health Surveys (DHS) program data archivist permitted the authors to download the dataset used in this investigation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthors\u0026rsquo; information\u003c/h2\u003e\u003cp\u003eAll authors are affiliated with the Department of Statistics, Jagannath University, Dhaka-1100, Bangladesh.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors received no funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJH (Jakir Hossain) conceptualized the study, developed the main statistical analysis, drafted the manuscript, wrote the main script, and wrote the introduction. ASMRR (Abu Sayeed Md. Ripon Rouf) reviewed the manuscript critically. MT (Muhammad Tareq) checked and proofread the final document. MR (Md. Rokunuzzaman) handled the data processing and management. SKDS (Samrat Kumar Dev Sharma) reviewed the final document and refined this. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the Demographic and Health Survey (DHS) Program for providing access to the dataset.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the DHS Program upon reasonable request and registration. Data access is granted at: [https://dhsprogram.com/data/available-datasets.cfm](https:/dhsprogram.com/data/available-datasets.cfm) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHossain MM, Abdulla F, Banik R, Yeasmin S, Rahman A. Child marriage and its association with morbidity and mortality of under-5 years old children in Bangladesh. PLoS ONE. Feb. 2022;17(2):e0262927. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0262927\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0262927\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSagalova V, et al. Levels and trends of adolescent marriage and maternity in West and Central Africa, 1986\u0026ndash;2017. J Glob Health. 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Geospatial analysis of childhood morbidity in Ghana, \u003cem\u003ePLOS ONE\u003c/em\u003e, vol. 14, no. 8, p. e0221324, Aug. 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0221324\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0221324\" 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":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Early marriage, Under-five morbidity, Child health, Socioeconomic determinants, Binary logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-7306650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7306650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEarly marriage and socioeconomic factors, which expose young mothers to early pregnancy under situations of adversity, as a result, dramatically raise the risk of children’s morbidity and perpetuate intergenerational cycles of poor health and disparity. Thus, this study is to assess the impact of early marriage and socioeconomic factors on children’s morbidity in South Asian countries using national survey data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods: \u003c/strong\u003eThis study utilized the most recent nationally representative Demographic and Health Survey (DHS) child datasets from five South Asian countries—Bangladesh, India, Pakistan, Afghanistan, and Nepal—comprising a total sample of 286,131 children. The study's outcome variable was the child morbidity. In addition to descriptive statistics, a two-stage binary logistic regression was used to analyze factors influencing child morbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn South Asia, Pakistan had the highest prevalence of child morbidity at 44.38%, followed by Afghanistan at 42.25%, Bangladesh at 34.20%, Nepal at 28.33%, and India with the lowest at 17.72%. Binary logistic regression revealed key factors associated with under-five morbidity in South Asia. Children born to early-married mothers in Pakistan had a significantly higher risk of morbidity (OR = 1.43, 95% CI: 1.20-1.70). Higher morbidity was also associated with maternal secondary education in Pakistan (OR = 1.88, 95% CI: 1.40-2.53), eight or more antenatal care visits in Pakistan (OR = 1.75, 95% CI: 1.17-2.63), Afghanistan (OR = 2.23, 95% CI: 1.83-2.71), and female-headed households in India and Pakistan (OR = 1.14, 95% CI: 1.05-1.24). Breastfeeding was connected to higher child morbidity in Bangladesh, India, Pakistan, and Afghanistan. In contrast, higher maternal education was associated with a significant reduction in child morbidity in both India (OR = 0.81, 95% CI: 0.70–0.93) and Afghanistan (OR = 0.57, 95% CI: 0.45–0.72). Rural residence (OR = 0.80, 95% CI: 0.73–0.87) in Afghanistan, as well as wealth status in India (OR = 0.79, 95% CI: 0.72–0.87), were protective factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings highlight the urgent need to delay early marriage and address socioeconomic disparities to reduce child morbidity in South Asia. Improving maternal education and access to healthcare is crucial for enhancing child health and well-being in the region.\u003c/p\u003e","manuscriptTitle":"Assessing the Impact of Early Marriage and Socioeconomic Determinants on Under-Five Morbidity: A Cross-Country Analysis in South Asia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:58:42","doi":"10.21203/rs.3.rs-7306650/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-09T05:47:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T18:21:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151176293108010939308505136075664339334","date":"2025-12-10T18:05:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T08:31:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324964046562612175833851906645379139750","date":"2025-11-23T11:24:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T13:46:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-11T11:58:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T09:14:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T09:13:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-08-06T06:55:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e1ce9c6-6a7b-4450-8809-bcfa2986db62","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:01:10+00:00","versionOfRecord":{"articleIdentity":"rs-7306650","link":"https://doi.org/10.1186/s12887-026-06596-x","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2026-02-10 15:58:13","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-08-13 05:58:42","video":"","vorDoi":"10.1186/s12887-026-06596-x","vorDoiUrl":"https://doi.org/10.1186/s12887-026-06596-x","workflowStages":[]},"version":"v1","identity":"rs-7306650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7306650","identity":"rs-7306650","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}